International Journal of Horticulture, 2026, Vol.16, No.3, 149-163 http://hortherbpublisher.com/index.php/ijh 162 Ramayya P., Vinukonda V., Singh U., Alam S., Venkateshwarlu C., Vipparla A., Dixit S., Yadav S., Abbai R., Badri J., T R., Padmakumari A., Singh V., and Kumar A., 2021, Marker-assisted forward and backcross breeding for improvement of elite Indian rice variety Naveen for multiple biotic and abiotic stress tolerance, PLoS One, 16(9): e0256721. https://doi.org/10.1371/journal.pone.0256721 Ravelombola W., Qin J., Shi A., Song Q., Yuan J., Wang F., Chen P., Yan L., Feng Y., Zhao T., Meng Y., Guan K., Yang C., and Zhang M., 2021, Genome-wide association study and genomic selection for yield and related traits in soybean, PLoS One, 16(8): e0255761. https://doi.org/10.1371/journal.pone.0255761 Riaz A., Raza Q., Kumar A., Dean D., Chiwina K., Phiri T., Thomas J., and Shi A., 2023, GWAS and genomic selection for marker-assisted development of sucrose enriched soybean cultivars, Euphytica, 219(9): 97. https://doi.org/10.1007/s10681-023-03224-y Ru S., Main D., Evans K., and Peace C., 2015, Current applications, challenges, and perspectives of marker-assisted seedling selection in Rosaceae tree fruit breeding, Tree Genetics and Genomes, 11(1): 8. https://doi.org/10.1007/s11295-015-0834-5 Shi A., Gepts P., Song Q., Xiong H., Michaels T.E., and Chen S., 2021, Genome-wide association study and genomic prediction for soybean cyst nematode resistance in USDA common bean (Phaseolus vulgaris) core collection, Frontiers in Plant Science, 12: 624156. https://doi.org/10.3389/fpls.2021.624156 Sinha D., Maurya A.K., Abdi G., Majeed M., Agarwal R., Mukherjee R., Ganguly S., Aziz R., Bhatia M., Majgaonkar A., Seal S., Das M., Banerjee S., Chowdhury S., Adeyemi S., and Chen J.T., 2023, Integrated genomic selection for accelerating breeding programs of climate-smart cereals, Genes, 14(7): 1484. https://doi.org/10.3390/genes14071484 Song L., Wang R., Yang X., Zhang A., and Liu D., 2023, Molecular markers and their applications in marker-assisted selection (MAS) in bread wheat (Triticum aestivumL.), Agriculture, 13(3): 642. https://doi.org/10.3390/agriculture13030642 Sun B., Guo R., Liu Z., Shi X., Yang Q., Shi J., Zhang M., Yang C., Zhao S., Zhang J., He J., Zhang J., Su J., Song Q., and Yan L., 2022, Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content, Frontiers in Plant Science, 13: 1064623. https://doi.org/10.3389/fpls.2022.1064623 Vargas-Almendra A., Ruiz-Medrano R., Núñez-Muñoz L.A., Ramírez-Pool J.A., Calderón-Pérez B., and Xoconostle-Cázares B., 2024, Advances in soybean genetic improvement, Plants, 13(21): 3073. https://doi.org/10.3390/plants13213073 Wang X., Qi Y., Sun G., Zhang S., Li W., and Wang Y., 2024, Improving soybean breeding efficiency using marker-assisted selection, Molecular Plant Breeding, 15: 1-9. https://doi.org/10.5376/mpb.2024.15.0025 Wang X., Zhang M., Li F., Liu X., Zhang C., Zhang F., Zhao K., Yuan R., Lamlom S., Ren H., Qiu H., and Zhang B., 2025, Genome-wide association study reveals key genetic loci controlling oil content in soybean seeds, Agronomy, 15(8): 1889. https://doi.org/10.3390/agronomy15081889 Xu Y., Yang W., Qiu J., Zhou K., Yu G., Zhang Y., Wang X., Jiao Y., Wang X., Hu S., Zhang X., Li P., Lu Y., Chen R., Tao T., Yang Z., Xu Y., and Xu C., 2025, Metabolic marker-assisted genomic prediction improves hybrid breeding, Plant Communications, 6(3): 101199. https://doi.org/10.1016/j.xplc.2024.101199 Xu Y., Zhang X., Li H., Zheng H., Zhang J., Olsen M., Varshney R., Prasanna B., and Qian Q., 2022, Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction, Molecular Plant, 15(11): 1664-1695. https://doi.org/10.1016/j.molp.2022.09.001 Xue Y., Tang X., Zhu X., Zhang R., Yao Y., Cao D., He W., Liu Q., Luan X., Shu Y., and Liu X., 2025, Leveraging GWAS-identified markers in combination with Bayesian and machine learning models to improve genomic selection in soybean, International Journal of Molecular Sciences, 26(19): 9586. https://doi.org/10.3390/ijms26199586 Yang Q., Zhang J., Shi X., Chen L., Qin J., Zhang M., Yang C., Song Q., and Yan L., 2023, Development of SNP marker panels for genotyping by target sequencing (GBTS) and its application in soybean, Molecular Breeding, 43(4): 26. https://doi.org/10.1007/s11032-023-01372-6 Yao D., Zhao Q., Li T., Wang J., Wang L., Liu Y., Hao W., and Liu H., 2022, Genetic analysis and quantitative trait locus mapping using the major gene plus polygene model for soybean [Glycine max (L.) Merr.] main quality trait, Legume Research - An International Journal, 45(1): 18-24. https://doi.org/10.18805/lrf-705 Yerzhebayeva R., Didorenko S., Amangeldiyeva A., Daniyarova A., Mazkirat S., Zinchenko A., and Shavrukov Y., 2023, Marker-assisted selection for early maturing E loci in soybean yielded prospective breeding lines for high latitudes of Northern Kazakhstan, Biomolecules, 13(7): 1146. https://doi.org/10.3390/biom13071146 Yáñez J.M., Barría A., López M.E., Moen T., Garcia B.F., Yoshida G.M., and Xu P., 2023, Genome-wide association and genomic selection in aquaculture, Reviews in Aquaculture, 15(2): 645-675. https://doi.org/10.1111/raq.12750
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