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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 rank-change 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.

The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.

Using large numbers of DNA markers to predict genetic merit [genomic selection (GS)] is a new frontier in plant and animal breeding programs. GS is now routinely used to select superior bulls in dairy cattle breeding. In forest trees, a few empirical proof of-concept studies suggest that GS could be successful. However, application of GS in forest tree breeding is still in its infancy. The major hurdle is lack of high throughput genotyping platforms for trees, and the high genotyping costs, though, the cost of genotyping will likely decrease in the future. There has been a growing interest in GS among tree breeders, forest geneticists, and tree improvement managers. A broad overview of pedigree reconstruction and GS is presented. Underlying reasons for failures of marker-assisted selection were summarized and compared with GS. Challenges of GS in forest tree breeding and the outlook for the future are discussed, and a GS plan for a cloned loblolly pine breeding population is presented. This review is intended for tree breeders, forest managers, scientist and students who are not necessarily familiar with genomic or quantitative genetics jargon.

Financial performance of the P. patula × P. tecunumanii, P. greggii × P. tecunumanii, P. taeda × P. tecunumanii hybrids and their parental species was studied for South Africa. A model was developed for use in determining the profitability of a tree-breeding program (TBP) with pine hybrids in commercial plantations. Growth measurement data were collected in four, 12-year-old genetic trials on Mondi and Sappi land holdings in South Africa. Growth models developed for P. patula and P. taeda in South Africa were used to infer models for the other taxa and to calculate the optimal financial rotation age at discount rates of 6 and 8%. Financial data on pine plantations were collected from different sources in South Africa. Optimal rotation lengths in this study were found to be between 12 and 16 years for pulpwood and 17 years for sawtimber. The model output shows the net present value (NPV), the internal rate of return, and the minimum area that a tree grower has to plant every year in order to justify the investment in a TBP. A stochastic approach with Monte Carlo simulation showed that the sensitivity of NPV to uncertainty in the wood price was greater than that for the planting, harvesting, and transport costs.

Large-scale tree improvement programs began in the 1950s. Tree improvement is now part of operational silviculture programs in many companies and countries around the world and tree breeding programs have produced very impressive results: (1) realized gains in plantations being established today of some 40–50 % in volume yield above unimproved material for many programs; (2) increased efficiencies in all aspects of breeding, selection, testing and deployment; and (3) a shortening of the generation interval by a factor of two from approximately 30 years in the first generation to less than 15 years today for pine programs. What about the future? What should tree breeders be thinking, planning and doing to ensure that results 60 years from now are even more impressive than those from the previous 60 years? Tree breeders today live in a rapidly changing world faced with: increasing demands for food, energy and water; globalization leading to an interconnectedness of markets and rapid spread of exotic organisms; climate change and its implications for genetic deployment; burgeoning technology in robotics, communications and molecular tools; shifting ownership patterns of forest land; and the real possibility of completely new forest products and markets in the future. Three ideas for “Breeding for Value in a Changing World” are: (1) adopt a robust philosophy that aims to ensure maximum value produced per ha even in a future world that will be quite different; (2) embrace technology at every phase in the tree improvement process; and (3) encourage interdisciplinary teams of scientists to solve complex problems that require expertise ranging from molecular to landscape scales.

Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.

Twelve years have passed since the early outlooks of applying genomic selection (GS) to forest tree breeding, initially based on deterministic simulations, soon followed by empirical reports. Given its solid projections for causing a paradigm shift in tree breeding practice in the years to come, GS went from a hot, somewhat hyped, topic to a fast-moving area of applied research and operational implementation worldwide. The hype cycle curve of emerging technologies introduced by Gartner Inc. in 1995, models the path a technology takes in terms of expectations of its value through time. Starting with a sudden and excessively positive “peak of inflated expectations” at its introduction, a technology that survives the “valley of disappointment” moves into maturity to climb the “slope of enlightenment”, to eventually reach the “plateau of productivity”. Following the pioneering steps of GS in animal breeding, we have surpassed the initial phases of the Gartner hype cycle and we are now climbing the slope of enlightenment towards a wide application of GS in forest tree breeding. By merging modern high-throughput DNA typing, time-proven quantitative genetics and mixed-model analysis, GS moved the focus away from the questionable concept of dissecting a complex, polygenic trait in its individual components for breeding advancement. Instead of trying to find the needle in a haystack, i.e., the “magic” gene in the complex and fluid genome, GS more efficiently and humbly “buys the whole haystack” of genomic effects to predict complex phenotypes, similarly to an exchange-traded fund that more efficiently “buys the whole market”. Tens of studies have now been published in forest trees showing that GS matches or surpasses the performance of phenotypic selection for growth and wood properties traits, enhancing the rate of genetic gain per unit time by increasing selection intensity, radically reducing generation interval and improving the accuracy of breeding values. Breeder-friendly and cost-effective SNP (single nucleotide polymorphism) genotyping platforms are now available for all mainstream plantation forest trees, but methods based on low-pass whole genome sequencing with imputation might further reduce genotyping costs. In this perspective, I provide answers to why GS will soon become the most efficient and effective way to carry out advanced tree breeding, and outline a simple pilot demonstration project that tree breeders can propose in their organization. While the fundamental properties of GS in tree breeding are now solidly established, strategic, logistics and financial aspects for the optimized adoption of GS are now the focus of attentions towards the plateau of productivity in the cycle, when this new breeding method will become fully established into routine tree improvement.

Genomic selection (GS) is expected to cause a paradigm shift in tree breeding by improving its speed and efficiency. By fitting all the genome-wide markers concurrently, GS can capture most of the ‘missing heritability’ of complex traits that quantitative trait locus (QTL) and association mapping classically fail to explain. Experimental support of GS is now required. The effectiveness of GS was assessed in two unrelated Eucalyptus breeding populations with contrasting effective population sizes (N e = 11 and 51) genotyped with > 3000 DArT markers. Prediction models were developed for tree circumference and height growth, wood specific gravity and pulp yield using random regression best linear unbiased predictor (BLUP). Accuracies of GS varied between 0.55 and 0.88, matching the accuracies achieved by conventional phenotypic selection. Substantial proportions (74–97%) of trait heritability were captured by fitting all genome-wide markers simultaneously. Genomic regions explaining trait variation largely coincided between populations, although GS models predicted poorly across populations, likely as a result of variable patterns of linkage disequilibrium, inconsistent allelic effects and genotype × environment interaction. GS brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement. Nevertheless population-specific predictive models will likely drive the initial applications of GS in forest tree breeding.

Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size ( N e ) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by N e and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when N e  ≤ 30, while up to 20 markers/cM are necessary for larger N e . Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.

Vegetative propagation of forest trees offers advantages to both tree breeders and the forest industry. This review will describe benefits, type of vegetative propagation, and its integration into breeding programmes. Of all of the different methods for vegetative propagation, only rooted cuttings and somatic embryogenesis (and the combined use of both) offer any practical methods for large-scale commercial use. However, it is very difficult to fully appreciate the overall level of activity of the research and application of somatic embryogenesis of forest trees. Publications and reports only highlight a small fraction of the ongoing work. To this end, a survey was conducted across Europe (under EU Research Infrastructure Concerted Action “Treebreedex”) to document the species involved, the state-of-the-art of somatic embryogenesis, its stage of development and its application in tree improvement programmes and to commercial forestry. The results of this survey are presented and discussed. In addition, this review presents the challenges (biological, economic, public acceptance and regulatory) and their relationships to European forestry. Finally, a strategy to promote the use of this technology is proposed.