Molecular Plant Breeding 2024, Vol.15, No.6, 403-416 http://genbreedpublisher.com/index.php/mpb 410 with 85 genes previously associated with disease resistance, providing valuable insights for marker-assisted breeding (Saini et al., 2021). Additionally, transcriptome analysis of wheat genotypes resistant and susceptible to wheat dwarf virus (WDV) revealed specific expression patterns of genes related to resistance, such as glycosyltransferase and MYB transcription factors, which are crucial for developing virus-resistant wheat varieties (Sharaf et al., 2023). 7.2 Applications of proteomics in understanding wheat disease resistance mechanisms Proteomics offers a comprehensive view of the protein landscape in wheat, helping to elucidate the mechanisms underlying disease resistance. By profiling the proteome, researchers can identify proteins that play critical roles in the plant's defense response. For example, proteomic studies have highlighted the importance of proteins such as oxalate oxidase, peroxidase, and superoxide dismutase in resistance to various diseases, including leaf rust and stem rust. These findings underscore the potential of proteomics to uncover novel resistance mechanisms and inform breeding strategies aimed at enhancing disease resistance in wheat (Alotaibi et al., 2020; Sehgal et al., 2023). 7.3 Metabolomics revealing metabolic pathways involved in wheat disease resistance Metabolomics provides insights into the metabolic pathways that contribute to disease resistance in wheat. By analyzing the metabolites produced in response to pathogen infection, researchers can identify key metabolic changes associated with resistance. For instance, metabolomic profiling has been used to study the response of wheat to biotic stress, revealing specific metabolites and pathways that are activated during pathogen attack (Kaur et al., 2021). This approach helps in understanding the biochemical basis of resistance and can guide the development of wheat varieties with enhanced metabolic resilience to diseases (Yang et al., 2021; Sen et al., 2023). 7.4 Integrating multi-omics for precision disease resistance breeding The integration of multi-omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, offers a powerful strategy for precision disease resistance breeding in wheat (Xu and Su, 2024). By combining data from different omics layers, researchers can construct comprehensive models of disease resistance mechanisms. For example, integrating transcriptomic and metabolomic data has provided deeper insights into herbicide resistance mechanisms in weeds, highlighting the potential of such approaches in crop science (Sen et al., 2023). Similarly, the integration of genomics with other omics has been shown to enhance the understanding of molecular regulator networks, facilitating the development of wheat varieties with improved resistance to multiple diseases (Miedaner et al., 2020; Yang et al., 2021). This holistic approach enables the identification of key genes, proteins, and metabolites involved in resistance, paving the way for the development of robust, disease-resistant wheat varieties (Alotaibi et al., 2020; Hafeez et al., 2020; Sehgal et al., 2023). 8 Challenges and Solutions in Genomic Breeding 8.1 Challenges of genomic complexity and polyploid genomes The wheat genome is notably large and complex, which poses significant challenges for genomic breeding. The hexaploid nature of wheat, with its three sets of homologous chromosomes, complicates the assembly and analysis of its genome. This complexity is further exacerbated by extensive structural rearrangements and introgressions from wild relatives, which are common in wheat due to its complex breeding history aimed at improving various traits such as disease resistance and yield (Walkowiak et al., 2020). Additionally, the presence of multiple gene copies can obscure the identification of functional genes and their variants, making it difficult to pinpoint specific genes responsible for desirable traits (Babu et al., 2020). 8.2 Addressing the impact of the environment on genomic breeding strategies Environmental factors significantly influence the effectiveness of genomic breeding strategies. The interaction between genotype and environment (GxE) can lead to variability in trait expression, complicating the selection process for disease-resistant varieties. For instance, traits that confer resistance in one environment may not be effective in another due to differences in pathogen populations and environmental conditions (Paux et al., 2022). To address this, extensive phenotyping across multiple locations and years is essential to train genomic models
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