Molecular Plant Breeding 2024, Vol.15, No.3, 132-143 http://genbreedpublisher.com/index.php/mpb 139 gene families, such as NB-LRR, WRKY, bZIP, and MYB, which are crucial for developing disease-resistant tree varieties (Younessi-Hamzekhanlu and Gailing, 2022). The use of functional markers (FMs) closely associated with phenotypic traits further enhances the precision of selecting resistance genes (Salgotra and Stewart, 2020). 4.1.2 Breeding for multi-pathogen resistance Breeding for multi-pathogen resistance involves the pyramiding of multiple resistance genes into a single genotype. This approach has been successfully applied in various crops and can be adapted for tree breeding. For instance, molecular marker-assisted gene pyramiding has been used to combine resistance genes for different pathogens, enhancing the overall disease resistance of the plant (Zheng et al., 2022). Genomics-assisted breeding also facilitates the selection of multi-disease resistance (MDR) QTLs, which are essential for developing tree varieties resistant to multiple pathogens (Miedaner et al., 2020). 4.2 Improving growth rates and yield 4.2.1 Selecting for fast-growing varieties MAS has been instrumental in selecting fast-growing tree varieties by identifying and utilizing markers linked to growth rate traits. The integration of genomic selection (GS) methods, which use information from all markers across the genome, has further accelerated the breeding process by predicting the performance of individuals for specific traits, including growth rates (Younessi-Hamzekhanlu and Gailing, 2022). This approach allows for the rapid selection of fast-growing genotypes, thereby reducing the breeding cycle time. 4.2.2 Yield optimization strategies Yield optimization in tree breeding involves the selection of genotypes with superior yield traits. MAS enables the identification of markers associated with high yield traits, facilitating the selection of high-yielding varieties. The use of QTL mapping and GWAS has identified several marker-trait associations that are crucial for yield improvement (Kumawat et al., 2020). Additionally, the application of GS methods enhances the accuracy and efficiency of selecting high-yielding genotypes, thereby optimizing yield in tree breeding programs (Younessi-Hamzekhanlu and Gailing, 2022). 4.3 Enhancing wood quality 4.3.1 Traits affecting wood quality Wood quality is influenced by various traits, including wood density, fiber length, and lignin content. MAS has enabled the identification of markers associated with these traits, facilitating the selection of superior wood quality genotypes. Functional markers (FMs) that are closely linked to wood quality traits have been developed, allowing for precise selection and breeding of trees with desirable wood properties (Salgotra and Stewart, 2020). 4.3.2 Breeding programs for superior wood traits Breeding programs aimed at enhancing wood quality have benefited from the integration of MAS and GS methods. By identifying and utilizing markers linked to superior wood traits, breeders can develop tree varieties with enhanced wood quality more efficiently. The use of high-throughput markers and genomic selection models has significantly increased the speed and accuracy of breeding programs focused on wood quality improvement (Salgotra and Stewart, 2020; Younessi-Hamzekhanlu and Gailing, 2022). 5 Challenges and Limitations of Marker-Assisted Selection While MAS holds great promise for accelerating tree breeding programs, several technical, biological, and socio-economic challenges must be addressed to fully realize its potential. Addressing these challenges will require concerted efforts from researchers, breeders, and policymakers to develop more efficient, cost-effective, and widely accepted MAS methodologies. 5.1 Technical challenges 5.1.1 Marker density and coverage One of the primary technical challenges in marker-assisted selection (MAS) is ensuring adequate marker density and coverage across the genome. In conifers, for instance, the rapid decrease in linkage disequilibrium and the
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