Cotton Genomics and Genetics 2025, Vol.16, No.5, 222-231 http://cropscipublisher.com/index.php/cgg 225 more easily (Guo et al., 2024). It is particularly good at identifying the pleiotropic loci where "one gene affects multiple traits simultaneously", which is an important entry point for understanding the common genetic basis of complex traits (Sahito et al., 2024). Not only that, MT-GWAS can also quantify the genetic relationships among different traits, thereby providing clues for subsequent breeding directions, such as which traits can be improved together and which may conflict with each other. However, it should also be noted that it is, after all, just one of the tools. Whether to adopt it or not still depends on the research objective and the characteristics of the trait itself. 4 Key Discoveries in Multi-Trait GWAS for Cotton 4.1 Identification of pleiotropic QTLs controlling both fiber traits and disease resistance Some genetic loci seem to control more than one aspect. Past multi-trait GWAS and multi-environment analyses have shown that there are many QTLS that can simultaneously regulate multiple fiber quality traits (such as length, strength, and fineness) and yield traits (Gowda et al., 2024). Some of them are even linked to production factors such as the weight of the bells, the proportion of the clothes, and the number of bells-indicating that these areas have a strong sense of "taking into account". Interestingly, these QTLS have been repeatedly detected in multiple environments, and this "stability" is highly practical for breeders (Su et al., 2020). Although most current studies focus more on the two traits of yield and fiber, it can still be observed that some QTLS are also associated with disease resistance. In other words, this kind of genetic overlap may become a breakthrough point for "simultaneous improvement of dual traits". However, it should also be noted that these QTLS do not perform consistently in all environments, and some effects are also related to conditions. This point cannot be ignored. 4.2 Novel candidate genes uncovered through MT-GWAS and their functional annotations Not all candidate genes can be easily identified by traditional methods, especially those whose functions are "hidden behind multiple traits". Using methods such as MT-GWAS, researchers screened out some candidate genes that play a key role in fiber development and yield expression. Genes such as GH_D06G2161, which begin to be expressed in the early ovulate stage, and Ghir_D11G020400, which encodes MATE exovation protein and is related to fiber elongation, are both regarded as relatively core regulators (Wang et al., 2022). Functional annotations and transcriptome data provide more context, indicating that most of these genes are involved in cell wall synthesis, signal transduction, and developmental processes directly linked to fibrogenesis (Li et al., 2020). These new discoveries may not be immediately applicable, but they undoubtedly provide many new targets for marker-assisted selection and subsequent biotechnological interventions. 4.3 Insights into genotype-by-environment interactions from multi-environment studies Just because a gene is stable in the laboratory doesn't mean it works equally well in the field. The research results of multi-environment GWAS remind us that the expression of QTL and candidate genes is actually greatly influenced by the environment, and sometimes this influence is quite "jumping" (Zhu et al., 2021). Some sites are very stable, and the effect is quite consistent no matter where they are planted. However, there are also many QTLS that only show obvious signs in certain regions or years, which makes it necessary to pay attention to the interaction between genes and the environment. For instance, for some loci related to fiber quality, the test results will completely change when the location is changed or the year is changed. This also explains why in the breeding process, making decisions based solely on data from a certain location may not be sufficient. Multi-environment verification is not an option but a necessary prerequisite. 5 Integration with Other Omics and Functional Genomics 5.1 Combining MT-GWAS with transcriptomics and metabolomics for gene prioritization It's hard to tell which gene is truly the "key player" just by relying on GWAS. At this point, if the results of MT-GWAS are combined with the data of transcriptome and metabolome, it will be clearer to determine which candidate genes are worth giving priority to. For instance, if a certain gene is not only genetically related to fiber quality or disease resistance, but also active in transcriptional expression and even involved in a certain metabolic pathway, then it is not "merely related", but "may actually be involved" (Sanches et al., 2024). Through co-expression analysis, metabolic pathway enrichment, and network-based integration methods, researchers can identify truly functional genes from multiple perspectives, not just the "possibilities" in a statistical sense.
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