Field Crop 2024, Vol.7, No.6, 325-333 http://cropscipublisher.com/index.php/fc 327 sequencing has also been explored to reduce sequencing costs while still providing valuable SNP data for breeding programs (Ashrafi et al., 2015). These advancements have accelerated the breeding process by allowing for the selection of desirable traits at the DNA level, thus improving the efficiency and effectiveness of cotton breeding programs (Kushanov et al., 2021). In summary, the evolution of genetic marker technologies in cotton has transitioned from traditional methods to sophisticated molecular techniques, with SSRs and SNPs being the most commonly used markers. High-throughput sequencing has further advanced marker discovery, enabling more efficient breeding strategies and the development of high-yielding, high-quality cotton cultivars. 3 Yield-Related Traits in Cotton and Their Genetic Basis 3.1 Key traits influencing cotton yield Cotton yield is influenced by several key traits, including boll size, lint weight, and plant height. Boll size is a critical determinant of yield as it directly affects the number of seeds and the amount of fiber produced per plant. Larger bolls generally contribute to higher yields (Hussain et al., 2019). Lint weight, which refers to the weight of the fiber after ginning, is another crucial trait, as it determines the quantity of usable fiber obtained from the cotton plant (Zhang et al., 2020). Plant height can also influence yield, as it affects the plant's ability to capture sunlight and its overall biomass production (Baytar et al., 2018). 3.2 Quantitative trait loci (QTLs) associated with yield components Numerous QTLs have been identified that are associated with yield components in cotton. For instance, a study identified 983 QTLs related to fiber quality and yield, with 198 being stable across different environments (Zhang et al., 2019). Another research effort mapped 73 yield-related QTLs, with 10 being stable across multiple environments, highlighting the genetic complexity of yield traits (Liu et al., 2022). Additionally, 134 QTLs were associated with fiber yield traits, with 39 being novel discoveries, indicating ongoing advancements in understanding the genetic basis of yield (Joshi et al., 2023). These QTLs are distributed across various chromosomes, with some clusters showing significant correlations with yield traits (Gu et al., 2020). 3.3 The relationship between marker-assisted breeding and yield traits Marker-assisted breeding (MAB) has become an essential tool in improving yield traits in cotton. By utilizing genetic markers linked to desirable traits, breeders can more efficiently select for high-yielding varieties. For example, the identification of stable QTLs and candidate genes through genome-wide association studies (GWAS) and other mapping techniques has facilitated the development of cotton lines with enhanced yield potential (Su et al., 2020). The integration of MAB in breeding programs has allowed for the precise selection of traits such as boll weight and lint percentage, leading to improved cotton varieties with higher yields (Diouf et al., 2018). This approach not only accelerates the breeding process but also increases the accuracy of selecting for complex traits like yield (Fan et al., 2018). In summary, understanding the genetic basis of key yield-related traits and leveraging marker-assisted breeding techniques are crucial for enhancing cotton yield. The identification of QTLs and their integration into breeding programs can significantly improve the efficiency and effectiveness of developing high-yielding cotton varieties. 4 Integration of Genetic Markers in Breeding Programs 4.1 Marker-assisted selection (MAS) for yield improvement Marker-assisted selection (MAS) has become a pivotal tool in cotton breeding, allowing for the precise selection of desirable traits at the DNA level, thereby accelerating the breeding process. MAS facilitates the identification and incorporation of quantitative trait loci (QTLs) associated with yield and fiber quality into breeding programs. This approach has been successfully used to develop high-yielding cotton cultivars with superior fiber quality and resistance to biotic and abiotic stresses (Sabev et al., 2020; Kushanov et al., 2021). For instance, the development of the 'Ravnaq' cotton cultivar series exemplifies the successful application of MAS, where specific SSR markers were used to transfer QTLs associated with fiber quality traits into elite cultivars (Darmanov et al., 2022).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==