Tree Genetics and Molecular Breeding 2024, Vol.14, No.2, 43-56 http://genbreedpublisher.com/index.php/tgmb 48 5.3 Emerging techniques: CRISPR and genome editing in trees Emerging genome editing technologies, particularly CRISPR/Cas9, have introduced new possibilities for precise genetic modifications in trees. CRISPR/Cas9 allows for targeted editing of specific genes, enabling researchers to study gene function and develop tree varieties with desirable traits. This technology has been successfully applied in several tree species to introduce mutations, knock out genes, and insert new genetic material (Bansal et al., 2018). For example, CRISPR has been used to modify genes involved in lignin biosynthesis in poplar, resulting in altered wood properties that are beneficial for biofuel production (Jinek et al., 2012). Additionally, CRISPR-based approaches have been employed to enhance disease resistance in trees by targeting genes associated with pathogen susceptibility (Hsu et al., 2014). The precision and efficiency of CRISPR make it a powerful tool for functional genomics studies and the development of genetically improved tree varieties. As this technology continues to advance, it holds great promise for addressing challenges in forestry, such as improving tree resilience to climate change and increasing productivity. 6 Translating Genomic Insights into Forestry Practices 6.1 From genome to phenome: implementing genomic discoveries in breeding Translating genomic insights into practical applications in forestry begins with the integration of genomic data into breeding programs. Genomic selection (GS) has emerged as a powerful tool to accelerate breeding cycle s and enhance the selection accuracy of complex traits. This method utilizes genome-wide marker information to predict the genetic value of individuals, thereby enabling the selection of superior genotypes at an early stage without the need for extensive field trials (Grattapaglia, 2017). For instance, GS has been successfully applied in Populus species to identify loci associated with disease resistance and growth traits, significantly reducing the breeding cycle time (Grattapaglia et al., 2018). The use of high-throughput sequencing technologies, such as genotyping-by-sequencing (GBS), facilitates the rapid acquisition of genetic data, making it possible to genotype large populations and implement GS effectively. By linking genomic data with phenotypic traits, breeders can make informed decisions, improving the efficiency and effectiveness of tree breeding programs (Ratcliffe et al., 2015). 6.2 Molecular breeding techniques and their applications in forestry Molecular breeding techniques, including marker-assisted selection (MAS) and genome-wide association studies (GWAS), have revolutionized the way breeding programs are conducted in forestry. MAS involves using molecular markers linked to desirable traits to select individuals for breeding, thereby increasing the precision and speed of selection. GWAS, on the other hand, identifies associations between genetic markers and phenotypic traits across the genome, providing insights into the genetic architecture of complex traits (Badenes et al., 2016). For example, in Eucalyptus grandis, GS models have been developed to predict growth and wood quality traits, demonstrating higher prediction accuracies compared to traditional methods (Figure 2) (Mphahlele et al., 2020). These molecular techniques are particularly valuable in forestry due to the long generation times and large size of trees, which make conventional breeding approaches time-consuming and resource-intensive. The integration of molecular markers into breeding programs enables the selection of superior genotypes at an early stage, thereby accelerating the development of improved tree varieties (Iwata et al., 2016). Figure 2 illustrates the genomic selection accuracy for growth and wood quality traits in Eucalyptus grandis. Each scatter plot shows the relationship between DGV and GEBV (grey dots), indicating the accuracy of the training set, and the relationship between EBV and GEBV (red dots), indicating the accuracy of the validation set. For traits like fiber length, fiber width, cellulose, S/G ratio, density, diameter, and height, the training and validation set r values are 0.62/0.97, 0.67/0.97, 0.60/0.95, 0.62/0.98, 0.54/0.97, 0.47/0.88, and 0.62/0.94, respectively. These results demonstrate high genomic selection efficiency and prediction accuracy for these traits in the validation set.
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