Plant Gene and Trait 2024, Vol.15, No.1, 23-32 http://genbreedpublisher.com/index.php/pgt 31 Acknowledgments I would like to thank Dr. X. J. Fang, Director of the Hainan Institute of Tropical Agricultural Resources, for his careful reading of the initial draft of this paper and for his profound suggestions for revision. I also express our gratitude to the two anonymous peer reviewers for their meticulous reviews and rigorous suggestions for revisions. Funding This project was funded by the Hainan Institute of Tropical Agricultural Resources under the contract for the research project "Screening and Breeding of Sugarcane Resources" (Grant No. H20230101). Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Anilkumar C., Sunitha N.C., Harikrishna Devate N.B., and Ramesh S., 2022, Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review, Planta, 256(5): 87. https://doi.org/10.1007/s00425-022-03996-y PMid:36149531 Chen C., Powell O., Dinglasan E., Ross E.M., Yadav S., Wei X., and Hayes B.J., 2023, Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits, The Plant Genome, 16(4): e20390. https://doi.org/10.1002/tpg2.20390 PMid:37728221 Cherubin M.R., Carvalho J.L.N., Cerri C.E.P., Nogueira L.A.H., Souza G.M., and Cantarella H., 2021, Land use and management effects on sustainable sugarcane-derived bioenergy, Land, 10(1): 72. https://doi.org/10.3390/land10010072 Hayes B.J., Wei X., Joyce P., Atkin F., Deomano E., Yue J., and Voss-Fels K.P., 2021, Accuracy of genomic prediction of complex traits in sugarcane, Theoretical and Applied Genetics, 134: 1455-1462. https://doi.org/10.1007/s00122-021-03782-6 PMid:33590303 Hoarau J.Y., Dumont T., Wei X., Jackson P., and D’hont A., 2022, Applications of quantitative genetics and statistical analyses in sugarcane breeding, Sugar Tech., 24(1): 320-340. https://doi.org/10.1007/s12355-021-01012-3 Luo T., Liu X., and Lakshmanan P., 2023, A combined genomics and phenomics approach is needed to boost breeding in sugarcane, Plant Phenomics, 5: 0074. https://doi.org/10.34133/plantphenomics.0074 PMid:37456081 PMCid:PMC10348406 Mahadevaiah C., Appunu C., Aitken K., Suresha G.S., Vignesh P., Mahadeva Swamy H.K., and Ram B., 2021, Genomic selection in sugarcane: current status and future prospects, Frontiers in Plant Science, 12: 708233. https://doi.org/10.3389/fpls.2021.708233 PMid:34646284 PMCid:PMC8502939 Meena M.R., Appunu C., Arun Kumar R., Manimekalai R., Vasantha S., Krishnappa G., and Hemaprabha G., 2022, Recent advances in sugarcane genomics, physiology, and phenomics for superior agronomic traits, Frontiers in Genetics, 13: 854936. https://doi.org/10.3389/fgene.2022.854936 PMid:35991570 PMCid:PMC9382102 Meena M.R., Kumar R., Chinnaswamy A., Karuppaiyan R., Kulshreshtha N., and Ram B., 2020, Current breeding and genomic approaches to enhance the cane and sugar productivity under abiotic stress conditions, 3 Biotech., 10(10): 440. https://doi.org/10.1007/s13205-020-02416-w PMid:33014683 PMCid:PMC7501393 Meuwissen T.H., Hayes B.J., and Goddard M., 2001, Prediction of total genetic value using genome-wide dense marker maps, genetics, 157(4): 1819-1829. https://doi.org/10.1093/genetics/157.4.1819 PMid:11290733 PMCid:PMC1461589 Sandhu K.S., Shiv A., Kaur G., Meena M.R., Raja A.K., Vengavasi K., and Kumar S., 2022, Integrated approach in genomic selection to accelerate genetic gain in sugarcane, Plants, 11(16): 2139. https://doi.org/10.3390/plants11162139 PMid:36015442 PMCid:PMC9412483
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