CGG2025v16n3

Cotton Genomics and Genetics 2025, Vol.16, No.3, 148-162 http://cropscipublisher.com/index.php/cgg 162 Li Z., Liu S., Conaty W., Zhu Q., Moncuquet P., Stiller W., and Wilson I., 2022, Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods, Heredity, 129(2): 103-112. https://doi.org/10.1038/s41437-022-00537-x Liu J.J., and Huang F.J., 2022, Current situation and problems and countermeasures of cotton production in Xinjiang, Cotton Sciences, 44(5): 15-19. Ma P.P., Zhao Z.Q., Zhu J.B., and Sun G.Q., 2021, Physiological and molecular mechanisms of drought and salt tolerance in cotton, Journal of Agricultural Science and Technology, 23(2): 27-36. https://doi.org/10.13304/j.nykjdb.2019.0718 Patil A.E., Deosarkar D., Khatri N., and Ubale A.B., 2023, A comprehensive investigation of Genotype-Environment interaction effects on seed cotton yield contributing traits in Gossypium hirsutum L. Using multivariate analysis and artificial neural network, Computers and Electronics in Agriculture, 211: 107966. https://doi.org/10.1016/j.compag.2023.107966 Peng Z., Li H., Sun G., Dai P., Geng X., Wang X., Zhang X., Wang Z.Z., Jia Y., Pan Z., Chen B.J., Du X., and He S., 2021, CottonGVD: a comprehensive genomic variation database for cultivated cottons, Frontiers in Plant Science, 12: 803736. https://doi.org/10.3389/fpls.2021.803736 Si Z.F., Jin S.K., Li J.Y., Han Z.G., Li Y.Q., Wu X.N., Ge Y.X., Fang L., Zhang T.Z., and Hu Y., 2022, The design, validation, and utility of the "ZJU CottonSNP40K" liquid chip through genotyping by target sequencing, Industrial Crops and Products, 188(Part A): 115629. https://doi.org/10.1016/j.indcrop.2022.115629 Sun Z.W., Gu Q.S., Zhang Y., Wang X.F., and Ma Z.Y., 2022, Research progress on cotton gene discovery and molecular breeding, Journal of Agricultural Science and Technology, 24(7): 32-38. Tan H., Tang B., Sun M., Yin Q., Ma Y., Li J., Wang P., Li Z., Zhao G., Wang M., Zhang X., You C., and Tu L., 2024, Identification of new cotton fiber-quality QTL by multiple genomic analyses and development of markers for genomic breeding, The Crop Journal, 12(3): 866-879. https://doi.org/10.1016/j.cj.2024.03.014 Viana J., Piepho H., and Silva F.F., 2016, Quantitative genetics theory for genomic selection and efficiency of breeding value prediction in open-pollinated populations, Scientia Agricola, 73(3): 243-251. https://doi.org/10.1590/0103-9016-2014-0383 Wu C., Luo J., and Xiao Y., 2024, Multi-omics assists genomic prediction of maize yield with machine learning approaches, Molecular Breeding,44(2): 14. https://doi.org/10.1007/s11032-024-01454-z Yan C., Li J., Feng Q., Luo J., and Luo H., 2024, ResDeepGS: a deep learning-based method for crop phenotype prediction, In: Proceedings of the 4th international conference on computational agriculture and bioinformatics, Springer Nature Singapore, Singapore, pp.470-481. https://doi.org/10.1007/978-981-97-5131-0_40 Yan J., and Wang X., 2022, Machine learning bridges omics sciences and plant breeding, Trends in Plant Science, 28(2): 199-210. https://doi.org/10.1016/j.tplants.2022.08.018 Zhang Q., and Wang Y., 2024, AI in biology: transforming genomic research with machine learning, Computational Molecular Biology, 14(3): 106-114. https://doi.org/10.5376/cmb.2024.14.0013 Zhang X.J., Liu S.M., Wu P., Xu W.Y., Yang D.Y., Ming Y.Q., Xiao S.H., Wang W.R., Ma J., Nie X.H., Gao Z., Lv J.Y., Wu F., Yang Z.G., Zheng B.X., Du P., Wang J.M., Ding H., Kong J., Aierxi A., Yu Y., Gao W., Lin Z.X., You C.Y., Lindsey K., Štajner N., Wang M.J., Wu J.H., Jin S.X., Zhang X.L., and Zhu L.F., 2025, A panoramic view of cotton resistance to Verticillium dahliae: from genetic architectures to precision genomic selection, iMeta, 4(3): e70029. https://doi.org/10.1002/imt2.70029 Zhao T., Guan X.Y., Hu Y., Zhang Z.Q., Yang H., Shi X.W., Han J., Mei H., Wang L.Y., Shao L., Wu H.Y., Chen Q.Q., Zhao Y.Y., Pan J.Y., Hao Y.P., Dong Z.Y., Long X., Deng Q., Zhao S.J., Zhang M.K., Zhu Y.M., Ma X.W., Chen Z.Q., Deng Y.Y., Si Z.F., Li X., Zhang T.Z., Gu F., Gu X.F., and Fang L., 2024, Population-wide DNA methylation polymorphisms at single-nucleotide resolution in 207 cotton accessions reveal epigenomic contributions to complex traits, Cell Research, 34(12): 859-872. https://doi.org/10.1038/S41422-024-01027-X Zhao T., Wu H.Y., Wang X.T., Zhao Y.Y., Wang L.Y., Pan J.Y., Mei H., Han J., Wang S.Y., Lu K.N., Li M.L., Gao M.T., Cao Z.Y., Zhang H.L., Wan K., Li J., Fang L., Zhang T.Z., and Guan X.Y., 2023, Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield, Cell Reports, 42(9): 113111. https://doi.org/10.1016/j.celrep.2023.113111

RkJQdWJsaXNoZXIy MjQ4ODYzNA==