CGG_2025v16n5

Cotton Genomics and Genetics 2025, Vol.16, No.5, 249-258 258 Wang H., Huang C., Guo H., Li X., Zhao W., Dai B., Yan Z., and Lin Z., 2015, QTL mapping for fiber and yield traits in upland cotton under multiple environments, PLoS ONE, 10(6): e0130742. https://doi.org/10.1371/journal.pone.0130742 Wang J.M., and Zhang J., 2024, Assessing the impact of various cotton diseases on fiber quality and production, Field Crop, 7(4): 212-221. https://doi.org/10.5376/fc.2024.07.0021 Wang P., Abbas M., He J., Zhou L., Cheng H., and Guo H., 2024, Advances in genome sequencing and artificially induced mutation provides new avenues for cotton breeding, Frontiers in Plant Science, 15: 1400201. https://doi.org/10.3389/fpls.2024.1400201 Wu H., Han R., Zhao L., Liu M., Chen H., Li W., and Li L., 2025, AutoGP: An intelligent breeding platform for enhancing maize genomic selection, Plant Communications, 6(4): 101240. https://doi.org/10.1016/j.xplc.2025.101240 Wu Y., Huang L., Zhou D., Fu X., Li C., Wei S., Peng J., and Kuang M., 2020, Development and application of perfect SSR markers in cotton, Journal of Cotton Research, 3(1): 21. https://doi.org/10.1186/s42397-020-00066-0 Xu S., Pan Z., Yin F., Yang Q., Lin Z., Wen T., Zhu L., Zhang D., and Nie X., 2020, Identification of candidate genes controlling fiber quality traits in upland cotton through integration of meta-QTL, significant SNP and transcriptomic data, Journal of Cotton Research, 3(1): 34. https://doi.org/10.1186/s42397-020-00075-z 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 Yan J., Xu Y., Cheng Q., Jiang S., Wang Q., Xiao Y., Yan J., and Wang X., 2021, LightGBM: Accelerated genomically designed crop breeding through ensemble learning, Genome Biology, 22(1): 271. https://doi.org/10.1186/s13059-021-02492-y Yang P., Sun X., Liu X., Wang W., Hao Y., Chen L., Liu J., He H., Zhang T., Bao W., Tang Y., He X., Ji M., Guo K., Liu D., Teng Z., Liu D., Zhang J., and Zhang Z., 2022, Identification of candidate genes for lint percentage and fiber quality through QTL mapping and transcriptome analysis in an allotetraploid interspecific cotton CSSLs population, Frontiers in Plant Science, 13: 882051. https://doi.org/10.3389/fpls.2022.882051 Yang W., Feng H., Zhang X., Zhang J., Doonan J., Batchelor W., Xiong L., and Yan J., 2020, Crop phenomics and high-throughput phenotyping: Past decades, current challenges and future perspectives, Molecular Plant, 13(2): 187-214. https://doi.org/10.1016/j.molp.2020.01.008 Zhang K., Kuraparthy V., Fang H., Zhu L., Sood S., and Jones D., 2019, High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using CottonSNP63K array in upland cotton (Gossypium hirsutumL.), BMC Genomics, 20(1): 889. https://doi.org/10.1186/s12864-019-6214-z Zhang T., Zhang N., Li W., Zhou X., Pei X., Liu Y., Ren Z., He K., Zhang W., Zhou K., Zhang F., Yang D., and Li Z., 2020, Genetic structure, gene flow pattern, and association analysis of superior germplasm resources in domesticated upland cotton (Gossypium hirsutumL.), Plant Diversity, 42(3): 189-197. https://doi.org/10.1016/j.pld.2020.03.001 Zhu S.J., and Luo M.T., 2024, Resistance management in cotton: Addressing Bt cotton efficacy, Field Crop, 7(5): 270-277. https://doi.org/10.5376/fc.2024.07.0027

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