Molecular Plant Breeding 2024, Vol.15, No.6, 403-416 http://genbreedpublisher.com/index.php/mpb 405 and bioinformatics has facilitated the creation of a comprehensive wheat resistance gene atlas. This atlas aims to capture the molecular components governing disease resistance and provide a dynamic, durable approach to R gene deployment. The integration of genomic data from hexaploid and tetraploid wheat, as well as their wild relatives, is expected to enhance the breeding and deployment of disease-resistant wheat varieties (Hafeez et al., 2021). Furthermore, the application of genomics-assisted breeding techniques has accelerated the resistance breeding process. By utilizing large populations analyzed with high-density marker arrays and extensive phenotyping, genomic selection models can be trained to predict breeding values of untested genotypes. This approach has been successfully applied to various pathosystems, including fusarium head blight and septoria blotch, demonstrating the potential of integrating genomic data for the improvement of disease resistance in wheat (Miedaner et al., 2020). The availability of a wheat reference and pan-genome has also facilitated the identification of structural variations and novel resistance QTLs, providing a comprehensive understanding of the genetic architecture underlying disease resistance (Dallinger et al., 2023). 3 Genomic Selection and Disease Resistance 3.1 Application of genomic selection in wheat breeding Genomic selection (GS) has revolutionized wheat breeding by enabling the rapid selection of superior genotypes and accelerating the breeding cycle. The advent of next-generation sequencing technologies has made genotyping cost-effective, thus making GS a feasible selection tool in plant breeding (Sun et al., 2020). GS uses a statistical model to estimate all marker effects for an individual simultaneously, determining a genome estimated breeding value (GEBV). This allows breeders to select for performance based on GEBVs in the absence of phenotypic data, which is particularly useful for complex traits such as disease resistance (Larkin et al., 2019). The implementation of GS in wheat breeding has shown significant potential in improving the rate of genetic gain, especially for complex quantitative traits, by accelerating breeding cycles compared to traditional approaches (Crossa et al., 2017; Sun et al., 2020). The success of GS in wheat breeding is attributed to its ability to incorporate all marker information in the prediction model, thereby avoiding biased marker effect estimates and capturing more of the variation due to small-effect quantitative trait loci (QTL) (Varshney et al., 2017). This comprehensive approach allows for more accurate predictions and selection, potentially leading to more rapid and lower-cost gains from breeding (Jannink et al., 2010). Additionally, GS has been successfully implemented for a number of key traits in wheat, including grain yield, grain quality, and quantitative disease resistance, such as that for Fusarium head blight (Larkin et al., 2019). The integration of GS into wheat breeding programs is still being explored, with many studies showing its potential to change wheat breeding through achieving higher genetic gain (Merrick et al., 2022). 3.2 Accelerating the selection of disease-resistant traits through genomic prediction Genomic prediction (GP) models play a crucial role in accelerating the selection of disease-resistant traits in wheat. By analyzing phenotypes and high-density marker scores, GP models predict the breeding values of lines in a population, thus facilitating the rapid selection of superior genotypes (Varshney et al., 2017). The accuracy of GP models is influenced by several factors, including the selection of prediction models, marker density, trait heritability, and the relationship between training and validation sets (Larkin et al., 2019). Recent advances in hyperspectral image technology combined with GS and pedigree-assisted breeding have further enhanced the accuracy of GP models (Crossa et al., 2017). The use of GP models in wheat breeding has shown promising results in predicting quantitative disease resistance. For instance, studies have demonstrated that GS models can accurately predict disease resistance traits such as stripe rust, with higher accuracies than traditional marker-assisted selection methods. The inclusion of fixed effects in low prediction scenarios has been shown to increase accuracy, indicating that GS can effectively predict quantitative disease resistance in the presence of both major and minor genes (Merrick et al., 2021). Overall, the integration of GP models into wheat breeding programs has the potential to significantly accelerate the selection
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