Cotton Genomics and Genetics 2025, Vol.16, No.5, 249-258 251 variations, while the A subgenome is more closely related to yield-related traits (Ma et al., 2021). Candidate genes such as GhACT1 and GhGASL3, which are known to be related to cell wall development, are usually identified from these stable QTLS (Huang et al., 2021). 3.3 Mining of genes related to disease resistance and stress tolerance To enhance disease resistance and stress tolerance, relying solely on phenotypic selection and breeding is far from sufficient. Now with high-throughput genotyping and resequencing technology, researchers have been able to more precisely locate genes such as GaGSTF9 that are related to Fusarium wilt resistance, and even identify some structural variations on the D subgenome that are related to wilt tolerance (Figure 1) (Du et al., 2018). These achievements did not emerge out of thin air. They were also supported by genome editing tools and functional verification methods, providing a reliable basis for actual breeding (Kumar et al., 2024). 4 Application of Genotyping in QTL Mapping and Association Studies in Cotton 4.1 Current status and challenges in QTL mapping Although high-density SNP arrays and sequencing typing techniques have brought many breakthroughs in cotton QTL mapping, such as constructing more complete genetic maps and identifying a large number of QTLS related to yield and fiber traits (Wang et al., 2015; Li et al., 2016), but there are no shortage of problems. For allotetraploid species like cotton, the genome is inherently complex, and the intraspecspecific polymorphism is not high. As a result, the localization resolution is always limited. Not to mention that the traits themselves are prone to environmental interference. Many QTLS only exhibit under specific conditions, and only a few can be stably expressed in different populations and environments (Diouf et al., 2018). In addition, the diversity of alleles in the paternal and maternal parents is not high either. This limitation of the genetic background restricts the discovery of new loci (Joshi et al., 2023). 4.2 Advances in GWAS based on high-throughput genotyping Compared with traditional QTL mapping, GWAS seems to be more popular in diverse materials. Through high-throughput typing platforms such as GBS, SNP chips, and SLAF-seq, researchers have identified many new QTLS and candidate genes in multiple cotton germplasm resources (Joshi et al., 2023). This type of method has a greater chance of identifying allelic variations because it can cover more recombination events. It is particularly suitable for mining those stable gene loci related to pleiotropy traits such as fiber quality and yield (Huang et al., 2021). If the results of GWAS are combined with transcriptome or functional omics data, the determination of candidate genes will also be more well-grounded. 4.3 Fine mapping of QTLs and identification of candidate genes To dig deeper based on the existing positioning, one has to rely on meticulous mapping. Generally, QTL segments that have appeared in multiple environments and multiple studies, or some recurrent QTL clusters, are selected first. Then, high-density labeling and phenotypic data are used to narrow down the target area (Tan et al., 2018; Yang et al., 2022). Candidate genes like GhACT1, GhGASL3, GhSCPL40, and GhPBL19, which have already been identified, were actually screened out by integrating QTL mapping, GWAS, and transcriptome results (Zhang et al., 2019; Wang et al., 2020; Xu et al., 2020; Zhu and Luo, 2024). This strategy provides more reliable targets for molecular breeding and MAS development of cotton. 5 Contribution of High-Throughput Genotyping to Marker Development and Utilization 5.1 Development of SNP/InDel and other marker types In recent years, genomic sequencing technology and high-throughput typing platforms have become increasingly mature, driving the development of various molecular markers. It is not difficult to understand why SNP markers are widely used-they are numerous, have strong co-dominance, and are convenient to operate on high-throughput platforms, making them very suitable for handling large-scale materials, especially when mapping complex trait maps (Shehzad et al., 2017). However, the practicality of InDel markers should not be overlooked. For instance, in hybrid breeding, when identifying fertility recovery genes, InDel can come in handy. In contrast, although SSR markers are old, they still perform well in terms of polymorphism and repeatability, and are still frequently used in DNA fingerprinting analysis, population genetic diversity research, etc. (Wu et al., 2020; Iqbal et al., 2023).
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