Field Crop 2024, Vol.7, No.6, 325-333 http://cropscipublisher.com/index.php/fc 331 study found 210 fiber quality QTLs and 73 yield-related QTLs, with several being stable across multiple environments. Additionally, novel genomic regions and candidate genes have been uncovered, providing a deeper understanding of the genetic basis for these traits. Genetic marker research plays a crucial role in achieving sustainable yield improvements in cotton by enabling the identification and utilization of key genetic loci associated with desirable traits. The use of high-density genetic maps and genome-wide association studies (GWAS) has facilitated the discovery of QTLs and candidate genes that can be targeted in breeding programs to enhance fiber quality and yield. This research supports the development of cotton varieties that are more resilient to environmental stresses, such as drought, thereby contributing to sustainable agricultural practices. To address future challenges in cotton breeding, a multidisciplinary approach is essential. Integrating genomics, phenomics, and environmental data can enhance our understanding of complex traits and improve breeding strategies. Collaboration between geneticists, agronomists, and data scientists can lead to the development of more robust cotton varieties that meet the demands of both productivity and environmental sustainability. By leveraging advances in molecular biology, bioinformatics, and field trials, the cotton industry can continue to innovate and adapt to changing global conditions. Acknowledgments We are grateful to Dr. Xie for critically reading the manuscript and providing valuable feedback that improved the clarity of the text. We express our heartfelt gratitude to the two anonymous reviewers for their valuable comments on the manuscript. 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 Ashrafi H., Hulse-Kemp A., Wang F., Yang S., Guan X., Jones D., Matvienko M., Mockaitis K., Chen Z., Stelly D., and Van Deynze A., 2015, A long‐read transcriptome assembly of cotton (Gossypium hirsutumL.) and intraspecific single nucleotide polymorphism discovery, The Plant Genome, 8(2): 1-14. https://doi.org/10.3835/plantgenome2014.10.0068 Aydın A., 2023, Determination of genetic diversity of some upland and sea island cotton genotypes using high-resolution capillary electrophoresis gel, Agronomy, 13(9): 2407. https://doi.org/10.3390/agronomy13092407 Baytar A., Peynircioğlu C., Sezener V., Basal H., Frary A., Frary A., and Doğanlar S., 2018, Genome-wide association mapping of yield components and drought tolerance-related traits in cotton, Molecular Breeding, 38: 1-16. https://doi.org/10.1007/s11032-018-0831-0 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 TehseenAzhar M., 2016, Molecular breeding of cotton, Intech Publishers, 2016: 123-166. https://doi.org/10.5772/64593 Conaty W., Broughton K., Egan L., Li X., Li Z., Liu S., Llewellyn D., MacMillan C., Moncuquet P., Rolland V., Ross B., Sargent D., Zhu Q., Pettolino F., and Stiller W., 2022, Cotton breeding in australia: meeting the challenges of the 21st century, Frontiers in Plant Science, 13: 904131. https://doi.org/10.3389/fpls.2022.904131 Constable G., Llewellyn D., Walford S., and Clement J., 2015, Cotton breeding for fiber quality improvement, Industrial Crops: Breeding for Bioenergy and Bioproducts, 2015: 191-232. https://doi.org/10.1007/978-1-4939-1447-0_10 Darmanov M., Makamov A., Ayubov M., Khusenov N., Buriev Z., Shermatov S., Salakhutdinov I., Ubaydullaeva K., Norbekov J., Kholmuradova M., Narmatov S., Normamatov I., and Abdurakhmonov I., 2022, Development of superior fibre quality upland cotton cultivar series ‘Ravnaq’ using marker-assisted selection, Frontiers in Plant Science, 13: 906472. https://doi.org/10.3389/fpls.2022.906472 Deng X., Gong J., Liu A., Shi Y., Gong W., Ge Q., Li J., Shang H., Wu Y., and Yuan Y., 2019, QTL mapping for fiber quality and yield-related traits across multiple generations in segregating population of CCRI 70, Journal of Cotton Research, 2: 1-10. https://doi.org/10.1186/s42397-019-0029-y
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