Cotton Genomics and Genetics 2025, Vol.16, No.1, 29-38 http://cropscipublisher.com/index.php/cgg 31 2.3 Combined impact on cotton productivity and quality Pests and diseases combined will reduce cotton production and deteriorate the fiber quality, thus affecting the economic benefits of cotton cultivation as a whole (Tarazi et al., 2019). Pathogens and pests not only reduce production, but also deteriorate the length, fineness, and strength of cotton fibers (Asif et al., 2024). Future climate change may make the situation worse because it will increase the types and number of pests and diseases, and even make some previously uncommon pests common (Ateeq-Ur-Rehman et al., 2020). Therefore, in order to continue to grow cotton well in the face of frequent pests and diseases, good management methods are needed, and varieties that are resistant to diseases and insects are also needed. 3 Genomic Tools for Resistance Loci Identification 3.1 Genome-wide association studies (GWAS) and linkage mapping In fact, at the beginning, when studying cotton resistance, many people relied more on traditional linkage maps. But as SNP chips became more and more popular, the resolution of GWAS has been significantly improved - especially when looking for quantitative trait loci (QTL) related to Verticillium wilt and Fusarium wilt, this method is very practical (Abdelraheem et al., 2019). Of course, GWAS alone is not a panacea, and it often needs to be combined with linkage map analysis. For example, in the study of bacterial wilt, these two methods were used together to find important loci such as BB-13, which can be used as molecular markers in breeding later (Gowda et al., 2022; Schoonmaker et al., 2023). However, the data differences between different experiments are also quite large. In order to solve this problem, researchers later used Meta-QTL analysis to integrate multiple studies and select consensus regions and candidate genes that are stable under different genetic backgrounds (Huo et al., 2023). 3.2 Transcriptomics and gene expression profiling Gene expression differences are complex, and there are changes in different tissues and at different time points. It is not easy to really figure out which genes are related to resistance. The emergence of TWAS and RNA-seq has solved some of the problems. These technologies allow us to see some key expression QTLs (eQTLs) and gene modules, especially those involved in immune response or controlling ROS levels. But expression data alone is not enough. It must be analyzed together with GWAS or QTL data to more confidently lock in those genes that are directly related to disease resistance. Some candidate genes are expressed completely differently in resistant and susceptible varieties, and there are also differences in sequence (Zhao et al., 2021). Of course, not all genes that seem useful can be successfully verified in the end. But functional verification methods such as gene silencing have confirmed that some genes do play a key role in resistance mechanisms (Cui et al., 2021). 3.3 Pan-genome and comparative genomic approaches Through pan-genomics and comparative genomics, we have a better understanding of the types of resistance genes in cotton and how they evolve. Studies have found that many resistance-related genes (RGA) tend to be concentrated in certain areas, and they change and evolve through some means, such as sequence exchange, tandem duplication or segment duplication (Chen et al., 2015). In addition, some comparative genomic analyses have also found that some gene fragments from wild cotton species have also entered the genome of cultivated species and may enhance disease resistance. Some new resistance mechanisms are also believed to be related to specific gene families (such as proteins with double TIR domains) (Zhang et al., 2023c). QTL integration analysis conducted under different research and environmental conditions helps to accurately identify those stable resistance sites and hotspots, which is very helpful for breeding work (Abdelraheem et al., 2017). 4 Key Loci Conferring Disease and Insect Resistance 4.1 NB-LRRgenes and R-gene clusters Not all resistance genes are so "high-profile", but NB-LRRdoes often appear in studies of cotton disease resistance, so people often call it "disease resistance gene". Some important discoveries actually come from GWAS and QTL mapping, especially when studying Verticillium wilt and Fusarium wilt, researchers have found many areas where NBS-LRR genes are concentrated (Zhang et al., 2015; Abdelraheem et al., 2019; Huo et al., 2023). Some of these areas correspond to only one disease, while others can resist several. Take GbCNL130 for example, it belongs to
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