Molecular Plant Breeding 2024, Vol.15, No.6, 403-416 http://genbreedpublisher.com/index.php/mpb 406 of disease-resistant traits, thereby improving the efficiency and effectiveness of breeding efforts (Wang et al., 2018). 3.3 Case studies: development of disease-resistant wheat varieties using genomic selection Several case studies have demonstrated the successful development of disease-resistant wheat varieties using genomic selection. One notable example is the use of GS to improve resistance to Fusarium head blight, a major disease affecting wheat. By incorporating GS into the breeding program, researchers were able to select for resistance based on GEBVs, leading to the development of wheat varieties with enhanced resistance to this disease (Larkin et al., 2019). Another study focused on optimizing GS models for selecting both major and minor genes for resistance to stripe rust. The results showed that GS models had higher accuracies than traditional marker-assisted selection methods, reaching an accuracy of 0.72 for disease severity (Merrick et al., 2021). In addition to these specific examples, the broader application of GS in wheat breeding has shown significant potential in improving disease resistance. For instance, the integration of GS into a two-part breeding strategy, differentiating between population improvement and product development, has been proposed to optimize the breeding pipeline and achieve higher genetic gain (Merrick et al., 2022). This approach allows for the rapid selection of disease-resistant traits within and across breeding cycles, ultimately leading to the development of superior wheat varieties with enhanced disease resistance. Overall, these case studies highlight the effectiveness of GS in developing disease-resistant wheat varieties and underscore the importance of integrating GS into wheat breeding programs (Larkin et al., 2019; Sun et al., 2020; Merrick et al., 2021). 4 Association Analysis and Disease Resistance Gene Discovery 4.1 Localization of disease resistance genes through genome-wide association studies (GWAS) Genome-Wide Association Studies (GWAS) have been instrumental in identifying genomic regions associated with disease resistance in wheat (Wang and Li, 2024). High-resolution GWAS has facilitated the fine mapping of quantitative trait loci (QTL) and the identification of candidate genes. For instance, a study involving 768 wheat cultivars identified 153 QTLs for traits such as leaf rust, yellow rust, powdery mildew, and cold tolerance, with 81 QTLs delimited to ≤1.0 Mb intervals (Pang et al., 2021). Another study identified 395 QTLs for 12 traits, including disease resistance, across seven environments, with 273 QTLs delimited to ≤1.0 Mb intervals (Pang et al., 2020). These findings underscore the effectiveness of GWAS in pinpointing specific genomic regions linked to disease resistance, thereby providing a foundation for further genetic analysis and breeding efforts. 4.2 Dissecting disease resistance through candidate gene association studies (CGAS) Candidate Gene Association Studies (CGAS) offer a focused approach to understanding the genetic basis of disease resistance by examining specific genes known to be involved in plant defense mechanisms. A study on wheat recombinant inbreds mapped over 50 loci representing various classes of defense response (DR) genes, such as oxalate oxidase, peroxidase, superoxide dismutase, chitinase, and thaumatin, to QTLs associated with resistance to diseases like tan spot, leaf rust, Karnal bunt, and stem rust (Faris et al., 1999). This approach revealed that many minor resistance QTLs might result from the action of DR genes, highlighting the efficiency of CGAS in identifying key genes involved in disease resistance. 4.3 Practical applications of association analysis in wheat disease resistance breeding The practical applications of association analysis in wheat breeding are extensive, ranging from identifying disease-resistant parent genotypes to developing genome-based prediction models. For instance, a study using a multi-trait restrictive linear phenotypic selection index (RLPSI) identified 22 parent genotypes with potential resistance to multiple diseases such as leaf rust, stripe rust, leaf spot, and common bunt (Figure 1) (Iqbal et al., 2022). Figure 1 illustrates the resistance of different genotypes to multiple diseases, including stripe rust, leaf rust, leaf spot, and common bunt, along with the distribution of grain yield. The study results indicate that the selected genotypes performed well in terms of both disease resistance and yield, contributing to crop improvement and the selection of disease-resistant varieties. Additionally, genomic prediction models based on GWAS data have shown high accuracy in predicting resistance to various diseases, promoting the selection of resistant varieties
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