Molecular Plant Breeding 2024, Vol.15, No.6, 403-416 http://genbreedpublisher.com/index.php/mpb 409 (Walkowiak et al., 2020). The integration of genomic data into breeding programs has facilitated the identification of multiple disease resistance (MDR) loci, which are crucial for developing cultivars with broad-spectrum resistance (Miedaner et al., 2019). Additionally, genome-wide association studies (GWAS) have mapped genomic regions associated with resistance to various diseases, providing valuable information for selecting parental combinations in breeding programs (Iqbal et al., 2022). The use of high-throughput genotyping platforms and next-generation sequencing (NGS) technologies has further accelerated the identification and deployment of resistance genes, enhancing the overall genetic diversity and resilience of wheat cultivars (Babu et al., 2020). Figure 2 Utilizing the R-gene atlas and pathogen diversity information to determine appropriate gene combinations (Adapted from Hafeez et al., 2021) Image caption: (A): By integrating pathogen effector and diversity information, optimal R-gene stacking strategies are calculated to maximize durable resistance; (B): The process of transferring single R genes or stacked R genes into cultivated varieties through traditional methods (crossbreeding and marker-assisted selection, MAS) or genetic transformation; (C): Dynamic diversity deployment strategies over different years, reducing the risk of pathogens developing resistance to these genes by rotating single or stacked R-gene varieties (Adapted from Hafeez et al., 2021) 7 Multi-Omics Integration Strategies 7.1 Transcriptomics in studying the regulation of resistance gene expression Transcriptomics has been instrumental in understanding the regulation of resistance gene expression in wheat. By analyzing differentially expressed genes (DEGs) in response to pathogen attacks, researchers can identify key genes involved in disease resistance. For instance, a study identified 194 DEGs from five transcriptome studies,
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