Field Crop 2024, Vol.7, No.6, 325-333 http://cropscipublisher.com/index.php/fc 330 genetic map with 6187 bin markers was used to identify novel QTLs for fiber quality and yield traits (Gu et al., 2020). The use of 5178 SNP markers in another study facilitated the identification of 110 QTLs for various traits (Diouf et al., 2018). These advancements in marker technology are crucial for improving the efficiency of marker-assisted selection (MAS) in cotton breeding programs (Zhang et al., 2019). In summary, the meta-analysis highlights the identification of significant markers and consistent QTLs across different studies, emphasizing their potential in improving cotton yield traits. The advancements in marker development and application are paving the way for more efficient breeding strategies. 7 Implications for Future Cotton Breeding 7.1 Integration of advanced technologies like CRISPR and genomic selection The integration of advanced technologies such as CRISPR and genomic selection holds significant promise for the future of cotton breeding. CRISPR/Cas9 technology can be utilized to edit genes associated with disease susceptibility and negative regulators of yield-related traits, thereby enhancing cotton's resilience and productivity (Rauf et al., 2019; Mubarik et al., 2020). Genomic selection, which leverages high-throughput genotyping and phenotyping data, can accelerate the breeding process by predicting the performance of breeding lines before field trials, thus improving the efficiency of developing high-yielding and high-quality cotton cultivars (Bolek et al., 2016; Billings et al., 2022). These technologies, when combined, can significantly enhance the precision and speed of breeding programs, leading to the development of superior cotton varieties that meet the demands of changing climates and market needs (Sabev et al., 2020; Yang et al., 2022). 7.2 Role of collaborative research and data sharing in accelerating marker utility Collaborative research and data sharing are crucial for maximizing the utility of genetic markers in cotton breeding. Programs like the CSIRO cotton breeding initiative demonstrate the benefits of partnerships between research institutions and commercial entities, which facilitate access to diverse genetic resources and advanced technologies (Conaty et al., 2022). By sharing genomic data and breeding outcomes, researchers can build comprehensive databases that enhance the understanding of marker-trait associations, thus improving the accuracy of marker-assisted selection (MAS) (Kushanov et al., 2021). Such collaborations can also help in standardizing methodologies and tools across different breeding programs, thereby accelerating the development and deployment of improved cotton varieties globally (Billings et al., 2022). 7.3 Recommendations for enhancing the practical application of markers in breeding To enhance the practical application of genetic markers in cotton breeding, several strategies can be recommended. First, the development of high-density genetic maps and the use of next-generation sequencing technologies can improve the identification and validation of quantitative trait loci (QTLs) associated with economically important traits (Constable et al., 2015; Bolek et al., 2016). Second, integrating marker-assisted selection with traditional breeding methods can help overcome the limitations of conventional approaches, such as the time-consuming nature of phenotypic selection (Sabev et al., 2020). Finally, investing in training programs for breeders to effectively use molecular tools and data analytics can ensure that the latest advancements in genomics are fully utilized in breeding programs (Rauf et al., 2019; Kushanov et al., 2021). These steps will facilitate the creation of elite cotton cultivars with enhanced yield, fiber quality, and stress resistance. In summary, the future of cotton breeding lies in the strategic integration of cutting-edge technologies, collaborative efforts, and the practical application of genetic markers. These approaches will collectively drive the development of cotton varieties that are not only high-yielding and resilient but also tailored to meet the specific needs of different growing environments and market demands. 8 Concluding Remarks The meta-analysis of yield-related genetic markers in cotton has revealed significant insights into the genetic architecture underlying fiber quality and yield traits. Across multiple studies, a variety of quantitative trait loci (QTLs) have been identified, with some being stable across different environments. For instance, one study identified 983 QTLs related to fiber quality and yield, with 198 being stable across 17 environments. Another
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