Cotton Genomics and Genetics 2025, Vol.16, No.5, 222-231 http://cropscipublisher.com/index.php/cgg 223 perform under different populations, environments and molecular means. It is hoped that it can provide some valuable references for the future cotton breeding strategies and research directions. 2 Genetic Basis of Fiber Quality and Disease Resistance in Cotton 2.1 Key quantitative traits associated with fiber strength, length, and fineness When it comes to the quality of cotton, there are several core indicators that cannot be ignored: strength, length and fineness-especially fineness, which is often measured by the Macron value. Although these traits are also greatly influenced by the environment, their heritability is actually not low, which also means that there is some room for breeding improvement. At present, researchers have identified many genetic loci related to these traits through GWAS and QTL mapping. Even on chromosomes such as Dt11 and At07, some "hotspots" have been found, one tending towards length and the other associated with intensity (Said et al., 2013). A more detailed look shows that genes like Gh_D09G2376 and Gh_D06G1908 in these regions are considered to play a significant role in the development of fibers (Liu et al., 2020). Of course, relying solely on traditional drawing methods is not precise enough. The later introduced MAGIC and CSSL groups went a step further by uncovering the hidden gene interactions, such as which additive interactions or superior effects were influencing fibrous traits (Qi et al., 2024), thus making the research closer to the actual genetic background. 2.2 Major diseases impacting cotton yield Yield and quality are often not technical issues but rather diseases that cause trouble first. Diseases like Fusarium wilt, Fusarium wilt and bacterial wilt have dealt a heavy blow to cotton fields. Among them, the most severe one might still be fusarium wilt. Not only will the yield decrease, but the fiber quality will also be affected (Wang et al., 2025). In recent years, researchers have screened out a number of genes related to resistance through GWAS and transcriptome methods, such as resistance sites like qVW-A01-2, as well as candidate genes like GhAMT2 and GhGT-3b_A04 (Figure 1). The mechanisms involved are not complicated either. It mainly focuses on lignin synthesis, salicylic acid signaling pathway and reactive oxygen species regulation (Mao et al., 2023). Both upland cotton and sea island cotton have actually made considerable efforts in this regard, especially in the selection of appropriate alleles or the introduction of dominant fragments from disease-resistant resources. Breeders have been making attempts (Li et al., 2023a). Although the process was not easy, the improvement in disease resistance has indeed shown initial results in some varieties. 2.3 Genetic overlap and independence between fiber and resistance traits It is indeed not easy to achieve both disease resistance and fiber quality. Their genetic regulation is neither completely independent nor completely overlapping. In other words, some gene loci act on both traits, while others act on only one (Li et al., 2023b). For instance, in some regions of the D subgenome, there are both QTLS that improve fiber and signals that regulate wilt resistance, which shows the possibility of "balancing both ends" (Ma et al., 2021). However, real breeding cases also show that sometimes improving one trait can drag down another, such as chain burdens or genetic trade-offs, which are hard to avoid. Nowadays, multi-trait GWAS and integrated genome technologies are gradually beginning to identify key genes or chromosomal regions that have the potential to achieve bitrait improvement. This provides a new idea for cultivating cotton that is both high-quality and disease-resistant in the future. Ultimately, however, how these overlapping areas can achieve their maximum effectiveness still depends on further in-depth research. 3 Principles of Multi-Trait Genome-Wide Association Studies (MT-GWAS) 3.1 Differences between single-trait GWAS and MT-GWAS methodologies Previous GWAS studies basically focused on only one trait at a time. Although this approach is intuitive, it is prone to missing some genes that play a role in multiple traits (Turley et al., 2018). Especially when dealing with complex traits influenced by multiple factors, the requirement for sample size becomes very high. Later, it was discovered that in fact many traits are genetically related. MT-GWAS was developed on this basis. It will analyze multiple related traits together and utilize the genetic correlations among them to enhance detection ability (Yoshida and Yanez, 2020). By this method, not only can the loci that were "missed by single trait GWAS" be identified, but also the common genetic mechanisms behind multiple traits can be revealed. However, not all traits
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