Molecular Plant Breeding 2024, Vol.15, No.3, 132-143 http://genbreedpublisher.com/index.php/mpb 142 biotechnologies like CRISPR and synthetic biology, along with precision breeding tools such as big data and remote sensing, holds promise for the future of tree breeding. The future of tree breeding with MAS looks promising as it continues to integrate with cutting-edge biotechnologies. CRISPR and other gene editing tools will enable more precise modifications, enhancing traits such as disease resistance and growth. Synthetic biology will introduce new capabilities for creating novel genetic constructs, further expanding the possibilities of tree improvement. Precision breeding technologies, including machine learning and remote sensing, will refine the selection process, making it faster and more accurate. These advancements will not only improve the efficiency of tree breeding programs but also contribute to sustainable forestry practices and environmental conservation. To fully realize the potential of MAS in tree breeding, continued research and collaboration are essential. Researchers should focus on optimizing strategies for updating prediction models and integrating validated functional genomics data to improve prediction accuracy. Collaborative efforts between geneticists, breeders, and bioinformaticians will be crucial for addressing the challenges associated with the complex genetic architectures of trees and other crops. Furthermore, international collaboration and data sharing will be vital for building comprehensive genomic databases and developing robust breeding programs that can adapt to changing environmental conditions. By fostering a collaborative research environment, the tree breeding community can accelerate the development of innovative solutions and ensure the sustainable improvement of tree species for future generations. MAS has already made significant contributions to tree breeding, and its future looks bright with the continued integration of new technologies and collaborative efforts. These advancements will not only enhance the efficiency and effectiveness of tree breeding programs but also contribute to global efforts in forestry conservation and resource management. Acknowledgments The authors extend sincere thanks to the peer reviewers for their valuable feedback. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Boopathi N., 2020, Marker-assisted selection (MAS), In: Genetic mapping and marker assisted selection, Springer, Singapore, pp.343-388. https://doi.org/10.1007/978-981-15-2949-8_9 Darmanov M., Makamov A., Ayubov M., Khusenov N., Buriev Z., Shermatov S., Salakhutdinov I., Ubaydullaeva K., Norbekov J., Kholmuradova M., Narmatov S., Normamatov I., and Abdurakhmonov I., 2022, Development of superior fibre quality upland cotton cultivar series ‘Ravnaq’ using marker-assisted selection, Frontiers in Plant Science, 13: 906472. https://doi.org/10.3389/fpls.2022.906472 Degen B., and Müller N., 2023, A simulation study comparing advanced marker-assisted selection with genomic selection in tree breeding programs, G3: Genes, Genomes, Genetics, 13(10): jkad164. https://doi.org/10.1093/g3journal/jkad164 PMid:37494068 PMCid:PMC10542556 Francisco F.R., Aono A.H., da Silva C.C., Gonçalves P.S., Junior, E.J.S., Le Guen V., Fritsche-Neto R., Souza L.M., and de Souza A.P., 2021, Unravelling rubber tree growth by integrating GWAS and biological network-based approaches, Frontiers in Plant Science, 12: 768589. https://doi.org/10.3389/fpls.2021.768589 PMid:34992619 PMCid:PMC8724537 Grattapaglia D., 2022, Twelve years into genomic selection in forest trees: climbing the slope of enlightenment of marker assisted tree breeding, Forests, 13(10): 1554. https://doi.org/10.3390/f13101554
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