Genotype by environment interaction (G×E) refers to the comparative performances of genotypes differing among environments, representing differences in genotype rankings or differences in the level of expression of genetic differences among environments. G×E can reduce heritability and overall genetic gain, unless breeding programmes are structured to address different categories of environments. Understanding the impact of G×E, the role of environments in generating G×E and the problems and opportunities is vital to efficient breeding programme design and deployment of genetic material. We review the current main analytical methods for identifying G×E: factor analytic models, biplot analysis and reaction norm. We also review biological and statistical evidence of G×E for growth, form and wood properties in forest species of global economic importance, including some pines, eucalypts, Douglas-fir, spruces and some poplars. Among these species, high levels of G×E tend to be reported for growth traits, with low levels of G×E for form traits and wood properties. Finally, we discuss possible ways of exploiting G×E to maximise genetic gain in forest tree breeding. Characterising the role of environments in generating interactions is seen as the basic platform, allowing efficient testing of candidate genotypes. We discuss the importance of level-of-expression interaction, relative to rankchange interaction, as being greater than in many past reports, especially for deployment decisions. We examine the impacts of G×E on tree breeding, some environmental factors that cause G×E and the strategies for dealing with G×E in tree breeding, and the future role of genomics.
Accurate inference of relatedness between individuals in breeding population contributes to the precision of genetic parameter estimates, effectiveness of inbreeding management and the amount of genetic progress delivered from breeding programs. Pedigree reconstruction has been proven to be an efficient tool to correct pedigree errors and recover hidden relatedness in open pollinated progeny tests but the method can be limited by the lack of parental genotypes and the high proportion of alien pollen from outside the breeding population. Our study investigates the efficiency of sib-ship reconstruction in an advanced breeding population of Eucalyptus nitens with only partially tracked pedigree. The sib-ship reconstruction allowed the identification of selfs (4% of the sample) and the exploration of their potential effect on inbreeding depression in the traits studied. We detected signs of inbreeding depression in diameter at breast height and growth strain while no indications were observed in wood density, wood stiffness and tangential air-dry shrinkage. After the application of a corrected sib-ship relationship matrix, additive genetic variance and heritability were observed to increase where signs of inbreeding depression were initially detected. Conversely, the same genetic parameters for traits that appeared to be free of inbreeding depression decreased in size. It therefore appeared that greater genetic variance may be due, at least in part, to contributions from inbreeding in these studied populations rather than a removal of inbreeding as is traditionally thought.
Genomic selection is expected to enhance the genetic improvement of forest tree species by providing more accurate estimates of breeding values through marker-based relationship matrices compared with pedigree-based methodologies. When adequately robust genomic prediction models are available, an additional increase in genetic gains can be made possible with the shortening of the breeding cycle through elimination of the progeny testing phase and early selection of parental candidates. The potential of genomic selection was investigated in an advanced Eucalyptus nitens breeding population focused on improvement for solid wood production. A high-density SNP chip (EUChip60K) was used to genotype 691 individuals in the breeding population, which represented two seed orchards with different selection histories. Phenotypic records for growth and form traits at age six, and for wood quality traits at age seven were available to build genomic prediction models using GBLUP, which were compared to the traditional pedigree-based alternative using BLUP. GBLUP demonstrated that breeding value accuracy would be improved and substantial increases in genetic gains towards solid wood production would be achieved. Cross-validation within and across two different seed orchards indicated that genomic predictions would likely benefit in terms of higher predictive accuracy from increasing the size of the training data sets through higher relatedness and better utilization of LD.
Open-pollinated (OP) mating is frequently used in forest tree breeding due to the relative temporal and financial efficiency of the approach. The trade-off is the lower precision of the estimated genetic parameters. Pedigree/sib-ship reconstruction has been proven as a tool to correct and complete pedigree information and to improve the precision of genetic parameter estimates. Our study analyzed an advanced generation Eucalyptus population from an OP breeding program using single-step genetic evaluation. The relationship matrix inferred from sib-ship reconstruction was used to rescale the marker-based relationship matrix (G matrix). This was compared with a second scenario that used rescaling based on the documented pedigree. The proposed single-step model performed better with respect to both model fit and the theoretical accuracy of breeding values. We found that the prediction accuracy was superior when using the pedigree information only when compared with using a combination of the pedigree and genomic information. This pattern appeared to be mainly a result of accumulated unrecognized relatedness over several breeding cycles, resulting in breeding values being shrunk toward the population mean. Using biased, pedigree-based breeding values as the base with which to correlate predicted GEBVs, resulted in the underestimation of prediction accuracies. Using breeding values estimated on the basis of sib-ship reconstruction resulted in increased prediction accuracies of the genotyped individuals. Therefore, selection of the correct base for estimation of prediction accuracy is critical. The beneficial impact of sib-ship reconstruction using G matrix rescaling was profound, especially in traits with inbreeding depression, such as stem diameter.
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