Cotton Genomics and Genetics 2024, Vol.15, No.2, 66-80 http://cropscipublisher.com/index.php/cgg 74 Wang et al. (2017) conducted a study using high-throughput sequencing technology and GWAS to analyze fiber quality traits in G. barbadense across multiple environments. They identified several QTLs significantly associated with fiber length and strength, explaining major variations in these traits. Further analysis of these QTL regions identified a series of candidate genes that were highly expressed in high-quality fiber varieties. Using gene editing technologies such as CRISPR/Cas9, researchers can target these genes to directly improve fiber traits (Wang et al., 2017). 6.3 Case study on boll weight QTL mapping Boll weight is a critical trait directly affecting the total yield of cotton. Traditional breeding methods for improving boll weight often rely on phenotypic selection, which is less efficient and time-consuming. Advances in genome sequencing technologies allow researchers to more accurately locate and identify QTLs associated with boll weight, providing support for efficient breeding. Fan et al. (2018) used specific locus amplified fragment sequencing (SLAF-seq) technology to construct a high-density genetic map and identified 18 stable QTLs for boll weight in upland cotton (Gossypium hirsutum). These QTLs were consistently detected across multiple environments, explaining a significant portion of the phenotypic variation in boll weight. And a comprehensive analysis was conducted on QTLs related to fiber quality and lint yield traits, which are crucial for cotton breeding. Figure 3 shows the QTLs for various traits identified in their study. The marker information from these QTLs provides important support for marker-assisted selection (MAS), enabling breeders to select individuals with desirable boll weight traits earlier and more accurately in the breeding process (Fan et al., 2018). Figure 3 QTL associated with fiber quality and lint yield traits (Adapted from Fan et al., 2018) Image caption: The figure demonstrates the genomic locations of QTLs associated with these traits, providing a visual representation of the genetic architecture underlying fiber quality and yield in cotton. These insights facilitate a more targeted approach to cotton breeding, allowing breeders to combine multiple desirable traits in new cultivars (Adapted from Fan et al., 2018) Abdelraheem et al. (2019) conducted a study using high-throughput genome sequencing and GWAS to explore boll weight and yield traits in Upland cotton varieties in the USA. They evaluated the resistance and yield traits of
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