Cotton Genomics and Genetics 2025, Vol.16, No.5, 249-258 255 in IBP not only enables cross-regional teams to share data and tools, but also allows analysis to be completed on the cloud. The integration of AI technology has further accelerated this process. Nowadays, many analyses no longer require manual operation; systems can run predictions and recommend mating combinations on their own (Xu et al., 2022; Crossa et al., 2025). The breeding process that used to rely on experience to be refined gradually can now be assisted by intelligent analysis for many decisions, which has significantly increased the speed and improved the accuracy. 8 Conclusion The progress of high-density SNP chip technology has indeed been rapid in recent years. Chips like CottonSNP40K, CottonSNP63K and CottonSNP80K have made the genotyping of cotton both fast and accurate, and not very costly. With the addition of sequencing-based methods, these tools have basically clarified the genetic information of various germplasm materials. Nowadays, there are no technical barriers to constructing saturation maps, locating QTLS, or conducting GWAS. This technological breakthrough has also made it more reliable to screen key trait loci, and the efficiency of marker-assisted selection has significantly improved. Moreover, once these high-throughput typing techniques are combined with functional omics research and mutant libraries, the genetic basis of cotton will be expanded, and the adaptability of germplasm will also extend outward. However, relying solely on classification is not enough. Truly complex traits, such as yield, quality and resistance, cannot be clearly explained by data at the omics level alone. Thus, multi-omics integration has become the mainstream direction nowadays. Bring in genomics, transcriptomics, phenomics and even environmental data to jointly analyze the mechanisms of trait formation. This kind of analytical method is also less likely to get stuck when it comes to the interaction between genotypes and the environment. More importantly, this combination of measures can push us to design "super cotton"-that is, varieties that not only produce a lot but also have strong resistance and good quality. Of course, achieving these is not merely about "measuring accurately". The accumulation of gene editing tools like CRISPR and pan-genome resources actually gives us the space to further adjust the target traits. As long as these variant information can be accurately identified and well utilized, the goal of what traits to improve next will be very clear. Ultimately, it still depends on platform-based solutions to catch up with these technological achievements. High-throughput data does bring abundant resources, but it also means the need for powerful data processing capabilities. The intelligent cloud breeding system might be one of the solutions to this problem: it can not only store data but also automatically analyze and assist in decision-making. Especially after the intervention of AI, the breeding process will become much simpler than before. As sequencing costs continue to fall, multi-omics integration methods may become a "routine operation". By then, the gap between genotypes and phenotypes will become smaller and smaller, and cotton breeding will be better able to cope with the dual challenges of climate change and global demand. Acknowledgments We thank Mr Z. Wu from the Institute of Life Science of Jiyang College of Zhejiang A&F University for his reading 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 Adunola P., Ferrão L., Benevenuto J., Azevedo C., and Munoz P., 2024, Genomic selection optimization in blueberry: data-driven methods for marker and training population design, The Plant Genome, 17(3): e20488. https://doi.org/10.1002/tpg2.20488 Billings G., Jones M., Rustgi S., Bridges W., Holland J., Hulse-Kemp A., and Campbell B., 2022, Outlook for implementation of genomics-based selection in public cotton breeding programs, Plants, 11(11): 1446. https://doi.org/10.3390/plants11111446 Bolek Y., Hayat K., Bardak A., and Azhar M., 2016, Molecular breeding of cotton, In: Cotton research, Intech Publishers, pp.123-166. https://doi.org/10.5772/64593
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