Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

Abstract

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.

Keywords: genomic selection (GS); marker assisted selection (MAS); quantitative genetics; realized genomic relationship; single nucleotide polymorphisms (SNP); tree breeding; whole-genome regression.

Figure 1

Genomic selection in forest trees. GS begins with the development of a predictive model for the traits of interest (Left panel), which are then used in the GS cycles (Right panel) and progressively updated. GS uses genome-wide markers whose effects on the phenotype are estimated concurrently in a large and representative “training population” of individuals without applying severe significance tests. Markers are retained as forecasters of phenotypes in prediction models to be later applied to “selection candidates” for which only genotypes are collected. The prediction models are cross-validated against a “validation population,” a set of individuals of the same reference population that were not used for the estimation of marker effects. Once a prediction model is shown to provide adequate accuracy, it can be used in the GS cycle. An array of selection candidates - full of half-sib families derived from crossing either the original elite parents of the training set, or elite individuals selected in the training set - are genotyped and have their breeding values (GEBV) and/or genotypic values (GEGV; additive + non-additive effects) estimated using the model developed earlier. Top ranked seedlings for GEBV are subject to early flower induction and inter-mated to create the next generation of breeding. Top ranked seedlings for GEGV are clonally propagated and tested in verification clonal trials where elite clones are eventually selected for operational plantation. Additionally, all or subsets of the already genotyped selection candidates are planted in experimental design and phenotyped at the target selection age to provide genotype and trait data for GS model updating as GS generations advance and climate changes.