Molecular Plant Breeding 2024, Vol.15, No.6, 403-416 http://genbreedpublisher.com/index.php/mpb 411 that can predict breeding values accurately under diverse environmental conditions (Miedaner et al., 2020). Additionally, integrating genomic data with environmental data can help in understanding GxE interactions and developing more robust breeding strategies (Thudi et al., 2020). 8.3 Overcoming technical costs and implementation barriers The high costs associated with genomic technologies and the need for specialized infrastructure and expertise are significant barriers to the widespread implementation of genomic breeding. Next-generation sequencing (NGS) and high-throughput genotyping platforms, while revolutionary, require substantial financial investment and technical know-how (Babu et al., 2020). To mitigate these costs, collaborative efforts and shared resources, such as the proposed wheat resistance gene atlas, can provide breeders with access to essential genomic data and tools, thereby reducing individual costs and accelerating the breeding process (Hafeez et al., 2021). Additionally, advancements in bioinformatics and the development of cost-effective genotyping assays, such as KASP assays, have made it more feasible to implement genomic selection in breeding programs (Babu et al., 2020). 9 Future Perspectives 9.1 Future trends in genomic breeding technologies The future of genomic breeding technologies in wheat is poised for significant advancements. The integration of high-throughput genomic tools, such as single nucleotide polymorphism (SNP) arrays and high-density molecular marker maps, will continue to enhance the precision and efficiency of breeding programs (Paux et al., 2022). The use of genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping will facilitate the identification of candidate genes associated with disease resistance and other agronomically important traits (Babu et al., 2020; Jabran et al., 2023). Additionally, the application of CRISPR/Cas-9 and other genome-editing technologies will enable precise modifications in the wheat genome, allowing for the development of varieties with enhanced resistance to diseases and environmental stresses (Li et al., 2021). The integration of machine learning and artificial intelligence in genomic prediction models is also expected to accelerate the breeding process by improving the accuracy of phenotype predictions from genotypic data (Thudi et al., 2020). 9.2 Accelerating disease resistance breeding through international collaboration and data sharing International collaboration and data sharing are critical for accelerating disease resistance breeding in wheat. The generation of multiple wheat genome assemblies has revealed extensive genomic diversity, which can be leveraged through collaborative efforts to improve disease resistance (Walkowiak et al., 2020). Projects like BREEDWHEAT have demonstrated the importance of sharing genomic resources and tools with the global wheat community to enhance breeding programs (Paux et al., 2022). Collaborative efforts can also facilitate the large-scale phenotyping and genotyping required for effective genomic selection, as seen in the development of multi-disease resistance (MDR) strategies (Miedaner et al., 2020). By pooling resources and expertise, international collaborations can address the challenges posed by evolving pathogens and environmental changes, ultimately leading to the development of more resilient wheat varieties (Nelson et al., 2017; Paux et al., 2022). 9.3 Prospects of genomic breeding in contributing to global food security Genomic breeding holds immense potential in contributing to global food security by developing wheat varieties that are more productive, resilient, and nutritionally enhanced. The application of genomics-assisted breeding has already shown promise in improving resistance to major diseases such as rusts, bunts, and smuts, which are significant threats to wheat production (Babu et al., 2020; Jabran et al., 2023). By incorporating diverse genetic resources and utilizing advanced breeding techniques, it is possible to develop wheat varieties that can withstand biotic and abiotic stresses, thereby ensuring stable yields under changing climatic conditions (Mondal et al., 2016; Thudi et al., 2020). Furthermore, the integration of genomic tools with conventional breeding methods can expedite the development of high-yielding, disease-resistant varieties, contributing to sustainable agriculture and global food security (Keller et al., 2018; Paux et al., 2022). The continued advancement and adoption of these technologies will be crucial in meeting the growing demand for wheat and addressing the challenges of food security in the coming decades.
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