Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

Abstract

Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.

Lignin engineering in forest trees: From gene discovery to field trials

“…Thus, substantial improvements in lignin content and composition of forest trees are possible by traditional breeding alone ( Studer et al., 2011 ). As traditional breeding is time consuming owing to long generation times and the fact that wood quality traits are typically best evaluated at rotation age ( Ahmar et al., 2021 ), tree breeding can benefit from the use of genomic information ( Figure 4 ). For instance, genomic selection has substantially accelerated the speed of forest tree breeding ( Grattapaglia et al., 2018 ; Howe et al., 2020 ; Lebedev et al., 2020 ; Ahmar et al., 2021 ; Grattapaglia, 2022 ).…”

“…As traditional breeding is time consuming owing to long generation times and the fact that wood quality traits are typically best evaluated at rotation age ( Ahmar et al., 2021 ), tree breeding can benefit from the use of genomic information ( Figure 4 ). For instance, genomic selection has substantially accelerated the speed of forest tree breeding ( Grattapaglia et al., 2018 ; Howe et al., 2020 ; Lebedev et al., 2020 ; Ahmar et al., 2021 ; Grattapaglia, 2022 ). This technique enables the prediction of multiple traits based on thousands of molecular markers and therefore depends on low-cost, high-throughput sequencing techniques ( Grattapaglia and Resende, 2011 ; Rambolarimanana et al., 2018 ; Grattapaglia, 2022 ).…”

Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection

“…This method is of considerable importance in the study of complex quantitative traits with low heritability (such as yield, disease resistance, and wood quality) that are affected by environment and expensive to measure and appear after a long time of growth. Early selection of elite genotypes using this method not only reduces the cost and amount of work of breeding programs, but also makes it possible to identify successful parental crossings that ultimately lead to desired allelic combinations in the progeny [ 19 , 67 , 91 , 92 ]. The GS method, unlike MAS, uses the data of all markers without considering the significance of the marker-trait relationship, and the magnitude of the QTL effect [ 93 ].…”

“…In general, several methods for calculating GEBV values are based on Bayesian (e.g., Bayesian A, B, Bayesian ridge regression and Bayesian least absolute shrinkage and selection operator) and BLUP (genomic and ridge regression) methods. The application of different methods depends on the conditions of the research in question, for example, when there is less kinship between the breeding and training population, the Bayesian method is preferred to GBLUP, and on the other hand, for traits having high heritability and traits that are controlled by a moderate number of genes, it is better to use the Bayesian A and B method, respectively [ 92 ]. Since GEBVs can be estimated in seedlings, the GS approach can play an essential role in increasing genetic gain per time unit and reducing the breeding period in forest trees [ 94 ].…”

CRISPR-Based Genome Editing and Its Applications in Woody Plants

“…The recently developed gene editing technology can recognize the precise editing of specific gene loci and shorten the breeding cycle [ 12 ]. As a result, the emergence of gene editing technology provides new ideas and methods for the genetic improvement of woody plants and the research of plant gene functions.…”