Field Crop 2024, Vol.7, No.6, 325-333 http://cropscipublisher.com/index.php/fc 329 using polymorphic SSR markers, leading to the development of new cotton cultivars with superior fiber quality (Darmanov et al., 2022). 5.2 Success stories and quantitative impact on yield improvement The application of MAS in cotton breeding has led to significant improvements in fiber quality and yield. For instance, the development of the 'Ravnaq' cultivar series demonstrated the successful transfer of QTLs associated with superior fiber traits, resulting in cultivars with stronger, longer, and more uniform fibers compared to their parent lines (Figure 3) (Darmanov et al., 2022). Additionally, the identification of novel marker-trait associations has provided new insights into the genetic basis of yield and fiber quality, facilitating the development of high-yielding cotton varieties (Li et al., 2016; Kumar et al., 2021). These advancements underscore the potential of MAS to accelerate breeding processes and achieve substantial genetic gains in cotton (Kushanov et al., 2021). Figure 3 Fibre staple lengths (mm) of ‘Ravnaq-1’ and ‘Ravnaq-2’ cultivar compared to controls lines (Adopted from Darmanov et al., 2022) 5.3 Challenges and lessons learned from real-world applications of MAS Despite the successes, implementing MAS in cotton breeding presents several challenges. One major issue is the negative correlation between yield and fiber quality, which complicates the simultaneous improvement of both traits (Constable et al., 2015; Shang et al., 2015). Additionally, the complexity of cotton's genetic architecture, with multiple QTLs influencing key traits, requires precise mapping and validation of markers to ensure effective selection (Deng et al., 2019). Lessons learned from these challenges highlight the importance of integrating MAS with traditional breeding methods and utilizing comprehensive genomic tools to overcome limitations and enhance breeding efficiency (Rafiq et al., 2016). In summary, while MAS has proven to be a powerful tool in cotton breeding, its success depends on careful planning, integration with conventional methods, and continuous refinement of genomic resources. 6 Key Insights from the Meta-Analysis 6.1 Compilation of significant markers linked to yield traits The meta-analysis compiled a comprehensive list of significant markers associated with yield traits in cotton. For instance, a study identified 983 QTLs related to fiber yield and quality, with 198 being stable across multiple environments (Zhang et al., 2019). Another research identified 53, 70, and 68 significant SNPLDB loci associated with boll number, boll weight, and lint percentage, respectively (Su et al., 2020). Additionally, 71 QTLs for fiber quality and yield traits were detected, with 16 being stable across different environments (Li et al., 2016). 6.2 Identification of consistent QTLs across different studies and environments Consistent QTLs were identified across various studies and environments, highlighting their stability and potential utility in breeding programs. For example, 24 stable QTLs for fiber quality and 12 for yield traits were identified in one study (Gu et al., 2020). Another study found 62 stable QTLs for fiber quality and 10 for yield-related traits across multiple environments (Liu et al., 2022). Furthermore, 30 QTLs were consistent in at least two environments, indicating their reliability (Diouf et al., 2018). 6.3 Emerging trends in marker development and application Emerging trends in marker development and application include the use of high-density SNP markers and genome-wide association studies (GWAS) to enhance the precision of QTL mapping. For instance, a high-density
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