Cotton Genomics and Genetics 2025, Vol.16, No.5, 222-231 http://cropscipublisher.com/index.php/cgg 229 germplasm resources with relatively high diversity, the "supplementary information" provided by these methods may change the way the entire genetic map is understood. That is to say, what was invisible before might be due to the fact that the tools we used were too limited. Another direction worth noting is to directly feed the results of MT-GWAS into the genomic selection (GS) process. This is actually like adding a "multi-trait brain" to predictive breeding. By integrating the identified pleiotropic loci and the genetic correlations between traits, the breeding model can optimize multiple objectives at once-such as taking into account fiber quality, disease resistance and yield. Once such models are established, both the breeding efficiency and the speed of genetic gain will be significantly enhanced. Moreover, this is also expected to shorten the "gap" between gene discovery and practical application. Acknowledgments The authors express their gratitude to Mr. Qi for providing valuable feedback that improved the clarity of the text. 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 Cheng J.H., and Zhang J., 2025, High-yield cotton cultivation practices in arid regions, Molecular Soil Biology, 16(1): 27-36. https://doi.org/10.5376/msb.2025.16.0003 Gowda S., Fang H., Tyagi P., Bourland F., Dever J., Campbell B., Zhang J., Abdelraheem A., Sood S., Jones D., and Kuraparthy V., 2024, Genome-wide association study of fiber quality traits in US upland cotton (Gossypium hirsutumL.), Theoretical and Applied Genetics, 137(9): 214. https://doi.org/10.1007/s00122-024-04717-7 Guo H., Li T., Shi Y., and Wang X., 2024, MTML: an efficient multitrait multilocus GWAS method based on the Cauchy combination test, Biometrical Journal, 66(6): e202300130. https://doi.org/10.1002/bimj.202300130 Khatiwada A., Yilmaz A., Wolf B., Pietrzak M., and Chung D., 2023, Multi-GPA-Tree: statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results, PLoS Computational Biology, 19(12): e1011686. https://doi.org/10.1371/journal.pcbi.1011686 Li Y., Si Z., Wang G., Shi Z., Chen J., Qi G., Jin S., Han Z., Gao W., Tian Y., Mao Y., Fang L., Hu Y., Chen H., Zhu X., and Zhang T., 2023a, Genomic insights into the genetic basis of cotton breeding in China, Molecular Plant, 16(4): 662-677. https://doi.org/10.1016/j.molp.2023.01.012 Li Y., Zhang X., Lin Z., Zhu Q., Li Y., Xue F., Cheng S., Feng H., Sun J., and Liu F., 2023b, Comparative transcriptome analysis of interspecific CSSLs reveals candidate genes and pathways involved in verticillium wilt resistance in cotton (Gossypium hirsutumL.), Industrial Crops and Products, 197: 116560. https://doi.org/10.1016/j.indcrop.2023.116560 Li Z., Wang P., You C., Yu J., Zhang X., Yan F., Ye Z., Shen C., Li B., Guo K., Liu N., Thyssen G., Fang D., Lindsey K., Zhang X., Wang M., and Tu L., 2020, Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton, New Phytologist, 226(6): 1738-1752. https://doi.org/10.1111/nph.16468 Liu W., Song C., Ren Z., Zhang Z., Pei X., Liu Y., He K., Zhang F., Zhao J., Zhang J., Wang X., Yang D., and Li W., 2020, Genome-wide association study reveals the genetic basis of fiber quality traits in upland cotton (Gossypium hirsutumL.), BMC Plant Biology, 20(1): 395. https://doi.org/10.1186/s12870-020-02611-0 Lozano A., Ding H., Abe N., and Lipka A., 2023, Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops, BMC Bioinformatics, 24(1): 399. https://doi.org/10.1186/s12859-023-05519-2 Ma Z., Zhang Y., Wu L., Zhang G., Sun Z., Li Z., Jiang Y., Ke H., Chen B., Liu Z., Gu Q., Wang Z., Wang G., Yang J., Wu J., Yan Y., Meng C., Li L., Li X., Mo S., Wu N., Chen L., Zhang M., Si A., Yang Z., Wang N., Wu L., Zhang D., Cui Y., Cui J., Lü X., Li Y., Shi R., Duan Y., Tian S., and Wang X., 2021, High-quality genome assembly and resequencing of modern cotton cultivars provide resources for crop improvement, Nature Genetics, 53(9): 1385-1391. https://doi.org/10.1038/s41588-021-00910-2 Mao H., Zhang W., Lü J., Yang J., Yang S., Jia B., Song J., Wu M., Pei W., Zhang B., Zhang J., Wang L., and Yu J., 2023, Overexpression of cotton Trihelix transcription factor GhGT-3b_A04 enhances resistance to Verticillium dahliae and affects plant growth in Arabidopsis thaliana, Journal of Plant Physiology, 283: 153947. https://doi.org/10.1016/j.jplph.2023.153947 Morabito A., De Simone G., Pastorelli R., Brunelli L., and Ferrario M., 2025, Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review, Journal of Translational Medicine, 23(1): 425. https://doi.org/10.1186/s12967-025-06446-x
RkJQdWJsaXNoZXIy MjQ4ODYzNA==