Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 250 6.3 Application of integrated image data and genetic maps for identifying novel resistance genes Some studies have taken a different approach, simply combining image data and genetic maps for analysis. By obtaining phenotypic characteristics through high-throughput imaging and combining GWAS and QTL localization, researchers identified new resistance loci and candidate genes on chromosomes such as 4A, 5D, 6B and 7A (Hu et al., 2019; Song et al., 2025). On this basis, KASP markers were also developed specifically for screening genotypes with stronger disease resistance. Although this method involves many steps, it has a high accuracy rate and good efficiency, and is especially suitable for the current breeding rhythm that emphasizes "fast, accurate and decisive". 7 Functional Validation and Breeding Applications of Resistance Genes 7.1 Expression profiling and mutant validation of candidate genes Sometimes, whether a gene is a "disease-resistant gene" or not cannot be determined merely by prediction; it also needs to be verified through experiments. In soybean research, people first use GWAS to screen out candidate genes and then observe whether the expression of these genes changes after pathogen infection. Immediately after that, it was time to "knock" - CRISPR/Cas9 was used to knock out the target gene, and as a result, the mutant was more prone to disease infection. However, after overexpressing this gene, the plant became more resistant to diseases instead (Dai et al., 2025). These results indicate that this gene does indeed play a key role in the defense mechanism. Although the above example is from soybeans, when it comes to wheat, corn, or even rice, a similar process is also used. 7.2 Potential of gene editing (e.g., CRISPR/Cas9) in functional studies of resistance The application of CRISPR/Cas9 goes far beyond "verifying individual genes". It is more like a precise tool that can achieve rapid and targeted genetic modification in crop breeding. Compared with traditional methods, this technology enables people to directly observe the performance of a certain gene after modification, whether it is more disease-resistant and whether it affects other traits. In practical research, this approach can not only knock out "problem genes", but also design new superior alleles (Khadgi et al., 2025; Pedrozo et al., 2025). Therefore, CRISPR/Cas9 is no longer merely a research tool but an indispensable weapon in the modern breeding system. 7.3 Strategies for introgressing favorable alleles into commercial cultivars through molecular breeding However, even if it is determined which gene is good and has strong resistance, it cannot just remain in the laboratory. The ultimate goal is still to bring these superior alleles into the varieties grown by farmers. At this point, molecular breeding comes in handy. Methods such as marker-assisted selection and gene aggregation have been repeatedly verified to efficiently superimpose multiple disease-resistant genes and breed varieties with more stable and broad-spectrum resistance (Li et al., 2020; Hafeez et al., 2021; Li et al., 2025). Of course, what is needed behind this is a complete genetic map and functional marker system. After all, in the face of increasingly "intelligent" pathogens, relying on just one gene is not sufficient. Multiple resistance sites need to work together to ensure stable crop yields without failure (Li et al., 2020; Hafeez et al., 2021). 8 Conclusion and Perspectives In recent years, the localization of genes related to wheat scab (FHB) resistance has made considerable progress through high-throughput phenotypic techniques and GWAS. This type of method may seem complex, but its core is actually to match the phenotypic expression with the genotype data, and then identify the gene loci related to resistance through association analysis. With the support of such large-scale data, the speed of finding genes has increased, the accuracy has also improved, and the corresponding molecular markers have become more and more numerous, providing a lot of convenience for subsequent breeding work. Especially in the area of identifying resistant traits, it used to rely on experience, but now it is more dependent on algorithms. However, it's not necessarily easy to say so. When the volume of data increases, problems follow. Phenotypic data from different platforms and environments often have deviations, and just organizing these data is a headache. Coupled with the interweaving of multiple omics information, it is simply impossible to handle the analysis without some technical expertise and interdisciplinary collaboration. Moreover, there is still a lack of a unified
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