Cotton Genomics and Genetics 2025, Vol.16, No.5, 222-231 http://cropscipublisher.com/index.php/cgg 226 Although this method is a bit troublesome, it is much more reliable than relying solely on GWAS. Integrating multi-omics is not merely for the sake of good-looking data, but for more accurately selecting targets and biomarkers that can be used for breeding. 5.2 Validation of candidate genes using CRISPR, RNAi, and overexpression studies Discovering the candidate gene is only the first step; verification is the key. Whether it's the quality of the fibers or their disease resistance, guessing is of no use. It depends on what the experiments say. Gene editing technologies like CRISPR/Cas9, or methods such as RNA interference (RNAi) and gene overexpression, are the "toolboxes" for verification. By knocking out, silencing or enhancing the expression of specific genes, researchers can see whether these genes are involved in certain biological processes, such as fiber development or disease resistance response (Yang et al., 2021). This kind of verification not only confirms the function of genes, but also turns "correlation" into "causality". To put it bluntly, it's about letting experiments help us put those ambiguous gene loci into practice and truly turn them into practical achievements for breeding. 5.3 Epigenomic and proteomic contributions to trait expression Not all trait differences can be answered from DNA variations. Sometimes, epigenetic modifications and protein changes are the key. For instance, epigenetic regulations such as DNA methylation or histone modification adjust gene expression in response to environmental changes. Sometimes, the impact of such regulation on disease resistance or fiber quality is even more significant than that of the sequence itself (Morabito et al., 2025). Proteomics, on the other hand, approaches it from a different perspective-it does not focus on how much gene is expressed, but rather on how the final product (protein) performs, whether modification occurs, and whether there is interaction. If this information can be integrated and analyzed together with MT-GWAS, the genetics, epigenetics and protein regulation that affect cotton traits can be pieced together into a more complete map at the "system level". This step is indispensable for understanding the formation mechanism of complex traits. 6 Case Study 6.1 Case overview: a breeding program targeting both superior fiber and wilt resistance When it comes to the true application of MT-GWAS in breeding, the domestic Gossypium barbadense project is a typical example. However, the initial difficulty of this project lies in the fact that there is more than one goal: not only to improve the quality of fibers, such as the indicators of length, strength and fabric fraction, but also to enhance the resistance to Fuswilt disease. Once this disease breaks out, it will have a great impact on both yield and quality. Traditional methods are often inefficient in dealing with such complex targets. The strategy adopted by this project is to first conduct large-scale resequencing and phenotypic analysis of different germplasms, and then further explore the genetic patterns on this basis. Ultimately, it is about identifying the genes behind complex traits and clarifying the relationships. 6.2 Deployment of MT-GWAS to detect co-localized QTLs for fiber and disease traits There are actually quite a few traits analyzed in this project-a total of 15, including those related to fibers, diseases, and yield traits. The final screened gene loci were most related to fiber quality, followed by disease resistance and yield (Figure 2) (Zhao et al., 2022). One of the key breakthroughs is that MT-GWAS helped discover those "co-localized QTLS" that have an impact on multiple traits, meaning that the same gene region may simultaneously be responsible for both fiber and disease resistance. Such multi-potency loci are easily overlooked in traditional single-trait analysis. More importantly, some genes are not determined solely by statistics. Researchers also used gene expression data and transgenic verification methods (such as VIGS) to gradually "confirm" the functions of these candidate genes. 6.3 Outcomes: candidate genes validated, germplasm selected, and yield improvements observed The final outcome of this breeding project is also quite clear: five key candidate genes have been functionally verified, involving not just a single trait, but multiple aspects such as disease resistance, fiber length, strength and garment size. With these achievements, subsequent lineage selection and marker-assisted breeding will have a "clear coordinate". The newly bred germplasm not only meets the fiber quality standards but also has significantly stronger disease resistance than before. The more practical benefit is that the output has gradually increased, and the overall breeding effect is closer to the market and agronomic demands.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==