Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding - Grattapaglia, Dario; Silva-Junior, Orzenil B.; Resende, Rafael T.; Cappa, Eduardo P.; Müller, Bárbara S. F.; Tan, Biyue; Isik, Fikret; Ratcliffe, Blaise; El-Kassaby, Yousry A. (Frontiers in Plant Science, 2018)
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 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.