Cotton Genomics and Genetics 2025, Vol.16, No.3, 117-125 http://cropscipublisher.com/index.php/cgg 123 the variation of traits can be unexpectedly large. Whether it is QTL positioning or genomic selection, they are reliable in theory, but once the environment changes, the model may not work. Therefore, G×E interaction is an unavoidable hurdle. To solve it, the experimental design must be more detailed, and multi-environment testing is indispensable. Another point is that environmental data cannot be only put in the report, but must be included in the model. Otherwise, the selected materials may fail to perform well in a different planting area. 8.2 Need for multi-trait and multi-environment prediction models Early prediction models were actually quite "simple" - one model for each trait, one model for each environment. But the reality is much more complicated than that. The breeding goal now is to "superimpose multiple traits", and environmental fluctuations are getting bigger and bigger. If we still follow the old method, the selected varieties may be good in one aspect, but poor in another. So what is needed now is a model that can look at multiple traits at the same time and take environmental changes into account. In this way, it is possible to take into account yield, quality, and stress resistance, and it is possible to select cotton varieties that perform stably in different regions and years. This is not to say that the model can solve everything, but it is indeed closer to actual needs than previous methods. 8.3 Prospects for integrating gene editing with marker-based breeding In the future, cotton can be improved by combining gene editing technology (such as CRISPR/Cas) with molecular marker breeding methods. Gene editing can precisely modify key genes found through QTL mapping and genomic selection, so that varieties with ideal traits can be cultivated more quickly. Combining these two methods may break through the current bottleneck of trait improvement, combine useful genes more quickly, and better cope with the challenges brought by complex genetic structure and environmental changes. Acknowledgments We would like to thank the anonymous peer review for their critical comments and revising suggestion. 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 Altaf M., Tatar M., Ali A., Liaqat W., Mortazvi P., Kayıhan C., Ölmez F., Nadeem M., Javed J., Gou J., Wang M., Umar U., Dasgan H., Kurt C., Yıldız M., Mansoor S., Dababat A., Çeliktaş N., and Baloch F., 2024, Advancements in QTL mapping and GWAS application in plant improvement, Turkish Journal of Botany, 48(7): 376-426. https://doi.org/10.55730/1300-008x.2824 Bassi F., Bentley A., Charmet G., Ortiz R., and Crossa J., 2016, Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.), Plant Science, 242: 23-36. https://doi.org/10.1016/j.plantsci.2015.08.021 Cao D., Xue Y.G., Tang X.F., Sun J.Q., Luan X.Y., Liu Q., Zhu Z.F., He W.J., and Liu X.L., 2024, Identification and application of yield-related QTLs in soybean based on GWAS, Molecular Plant Breeding, 15(6): 371-378. http://dx.doi.org/10.5376/mpb.2024.15.0035 Chen Z., Klingberg A., Hallingbäck H., and Wu H., 2022, Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce, BMC Genomics, 24(1): 147. https://doi.org/10.1186/s12864-023-09250-3 Crossa J., Pérez-Rodríguez P., Cuevas J., Montesinos-López O., Jarquín D., De Los Campos G., Burgueño J., González-Camacho J., Pérez-Elizalde S., Beyene Y., Dreisigacker S., Singh R., Zhang X., Gowda M., Roorkiwal M., Rutkoski J., and Varshney R., 2017, Genomic selection in plant breeding: methods, models, and perspectives, Trends in Plant Science, 22(11): 961-975. https://doi.org/10.1016/j.tplants.2017.08.011 Daware A., Parida S., and Tyagi A., 2020, Integrated genomic strategies for cereal genetic enhancement: combining QTL and association mapping, In: Cereal genomics: methods in molecular biology, 2072: 15-25. https://doi.org/10.1007/978-1-4939-9865-4_3 Diouf L., Magwanga R., Gong W., He S., Pan Z., Jia Y., Kirungu J., and Du X., 2018, QTL mapping of fiber quality and yield-related traits in an intra-specific upland cotton using genotype by sequencing (GBS), International Journal of Molecular Sciences, 19(2): 441. https://doi.org/10.3390/ijms19020441 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
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