Molecular Plant Breeding 2024, Vol.15, No.5, 247-258 http://genbreedpublisher.com/index.php/mpb 256 Future research should focus on several key areas to further enhance the integration of QTL mapping and GS in the breeding of E. ulmoides. Although numerous QTLs have been well identified, their validation across different populations and environments is imperative to ensure their reliability and applicability in diverse breeding programs. It will be essential to develop and optimize GS models that incorporate multi-trait and multi-environment data. This includes utilizing high-throughput phenotyping and deep learning approaches to improve prediction accuracy. Additionally, identifying and characterizing candidate genes within QTL regions will provide insights into the biological mechanisms controlling important traits, which can be achieved through transcriptomic and proteomic analyses. Traditional breeding programs need to be restructured to effectively implement genomic selection. This includes reorganizing field designs, increasing the number of lines evaluated, and optimizing training populations. The integration of QTL mapping and GS represents a significant advancement in the breeding of E. ulmoides. By combining high-density genetic maps with sophisticated GS techniques, we can achieve more accurate and efficient selection of desirable traits. This approach not only accelerates the breeding process but also enhances the genetic gain and overall quality of E. ulmoides cultivars. Ongoing research and optimization in this field will undoubtedly contribute to the sustainable development and economic viability of this important tree species. Acknowledgments GenBreed Publisher appreciates the two anonymous peer reviewers for their constructive suggestions during the review process. Funding This work was supported by the Guizhou Academy of Agricultural Sciences Talent Special Project (No. 2023-02 and 2024-02), National Major Project of Cultivating New Varieties of Genetically Modified Organisms [grant no. 2016ZX08010003- 009], Guizhou Provincial Science and Technology Foundation (ZK [2024] General 532), Talent Base for Germplasm Resources Utilization and Innovation of Characteristic Plant in Guizhou (RCJD2018-14). 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 Aabidine A., Charafi J., Grout C., Doligez A., Santoni S., Moukhli A., Jay-Allemand C., Modafar C., and Khadari B., 2010, Construction of a genetic linkage map for the olive based on AFLP and SSR markers, Crop Science, 50: 2291-2302. https://doi.org/10.2135/CROPSCI2009.10.0632 Clark S., Hickey J., and Werf J., 2011, Different models of genetic variation and their effect on genomic evaluation, Genetics Selection Evolution, 43: 18. https://doi.org/10.1186/1297-9686-43-18 Desta Z., and Ortiz R., 2014, Genomic selection: genome-wide prediction in plant improvement, Trends in Plant Science, 19(9): 592-601. https://doi.org/10.1016/j.tplants.2014.05.006 Du Q., Wu Z., Liu P., Qing J., He F., Du L., Sun Z., Zhu L., Zheng H., Sun Z., Yang L., Wang L., and Du H., 2023, The chromosome-level genome of Eucommia ulmoides provides insights into sex differentiation and α-linolenic acid biosynthesis, Frontiers in Plant Science, 14: 1118363. https://doi.org/10.3389/fpls.2023.1118363 Eeuwijk F., Boer M., Totir L., Bink M., Wright D., Winkler C., Podlich D., Boldman K., Baumgarten A., Smalley M., Arbelbide M., Braak C., and Cooper M., 2009, Mixed model approaches for the identification of QTLs within a maize hybrid breeding program, Theoretical and Applied Genetics, 120: 429-440. https://doi.org/10.1007/s00122-009-1205-0 Goddard M., and Hayes B., 2007, Genomic selection, Journal of Animal Breeding and Genetics, 124(6): 323-330. https://doi.org/10.1111/J.1439-0388.2007.00702.X Grattapaglia D., and Resende M., 2011, Genomic selection in forest tree breeding, Tree Genetics & Genomes, 7: 241-255. https://doi.org/10.1007/s11295-010-0328-4 Habier D., Fernando R., and Dekkers J., 2007, The impact of genetic relationship information on genome-assisted breeding values, Genetics, 177: 2389-2397. https://doi.org/10.1534/genetics.107.081190
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