Cotton Genomics and Genetics 2025, Vol.16, No.3, 117-125 http://cropscipublisher.com/index.php/cgg 118 candidate genes, and specific breeding methods, especially those strategies that can improve both traits together. The application of genomic tools in actual breeding programs has very good prospects and is expected to break the traditional problem of "you can't have your cake and eat it too" and achieve a win-win situation and continuous improvement in yield and quality. 2 Quantitative Trait Loci (QTL) Mapping in Cotton 2.1 Fundamentals of QTL mapping and its role in dissecting complex traits In cotton breeding, traits such as yield and fiber quality are simple to say, but they involve many genes and complex environmental factors. One gene alone cannot explain too many problems. Scientists usually build a genetic map first, which is made up of many molecular markers. Why do they do this? Because this way they can find out which gene regions are related to the target traits step by step. QTL positioning is a method that comes in handy in this process. It is actually a genetic analysis method used to locate gene regions that affect specific traits. Of course, it is not a panacea, but when faced with complex traits, this tool is indeed much better than traditional methods (Sun et al., 2012). 2.2 Key yield- and fiber-related QTLs identified in diverse cotton populations Many researchers have found QTLs related to yield and fiber in different cotton varieties and planting environments. These QTLs are stable and have obvious effects on traits. For example, a study used a high-density genetic map to find more than 100 QTLs related to fiber quality, some of which were found multiple times in different generations and environments, indicating that they are reliable (Jia et al., 2024). Chromosome regions such as A7, D8 and C7 are "hotspots" for fiber length, strength and yield, and have been highlighted (Jamshed et al., 2016). Some QTL regions affect both yield and fiber quality, but their effects may be in opposite directions, indicating that there is a certain contradiction and balance between these traits (Figure 1) (Zhang et al., 2019b). QTLs that are stable in different environments are particularly suitable for marker-assisted selection and are of great value for breeding. Figure 1 Detailed information about the QTLs and QTL clusters. (a) The position of the QTLs on the consensus genetic map. (b) The distribution of the stable QTLs for the six traits on the 26 chromosomes. (c) The position of QTL clusters on the consensus genetic map of At. (d) The position of QTL clusters on the consensus genetic map of Dt. (e) The number of two-pair-trait QTL clusters with the same and different direction for additive effect (Adopted from Zhang et al., 2019b)
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