Maize Genomics and Genetics 2024, Vol.15, No.3, 111-122 http://cropscipublisher.com/index.php/mgg 119 6.5 Global collaboration and data sharing Global collaboration and data sharing are vital for the success of genomics-assisted breeding in maize. The integration of multi-disciplinary technologies, including Big Data and artificial intelligence, requires extensive collaboration among researchers, breeders, and institutions worldwide (Jiang et al., 2019). Sharing genomic and phenotypic data across borders will accelerate the breeding process and enhance the development of superior maize cultivars. Collaborative efforts will also facilitate the exchange of knowledge and resources, ensuring that advancements in genomic technologies benefit a wide range of stakeholders. In conclusion, the future of genomics-assisted breeding in maize is promising, with significant advancements in genomic technologies, multi-omics integration, precision breeding, and global collaboration. Addressing regulatory and ethical considerations will be crucial to ensure the responsible and equitable use of these technologies. By leveraging these advancements, maize breeding can achieve greater efficiency, resilience, and productivity, ultimately contributing to global food security. 7 Concluding Remarks Genomics-assisted breeding (GAB) has revolutionized maize breeding by leveraging modern genomic tools to enhance germplasm and develop superior cultivars. The integration of genomic selection, genome optimization, and advanced phenotyping has significantly accelerated the breeding process, enabling the development of climate-resilient and high-yielding maize varieties. Key advancements include the use of high-density marker arrays for genomic selection, the incorporation of doubled haploid production and genome optimization, and the development of cost-effective genotyping platforms. These technologies have collectively improved the efficiency and accuracy of breeding programs, resulting in substantial genetic gains and the creation of maize cultivars with enhanced disease resistance, yield, and nutritional quality. The future of maize breeding lies in the continued evolution and integration of genomics-assisted breeding techniques. The next phase, often referred to as GAB 2.0, will focus on the targeted manipulation of allelic variation to create novel diversity and optimize crop genomes. This will involve the use of advanced computational models and artificial intelligence to predict and design optimal genotypes. Additionally, the incorporation of multi-disease resistance (MDR) QTL and the development of climate-smart cultivars will be crucial in addressing the challenges posed by global climate change. The use of affordable and high-throughput genotyping platforms will further democratize access to advanced breeding technologies, enabling smaller breeding programs and developing countries to participate in the global effort to enhance food security. Overall, the future of maize breeding will be characterized by a more precise, efficient, and inclusive approach to developing superior maize cultivars. Funding This work was jointly supported by the Science and Technology Development Plan Project of Jilin Province (#20240303004NC), Innovation Capacity Building Project of Jilin Development and Reform Commission (#2023C035-3) and the Science and Technology Development Plan Project of Jilin City (#20230501010). Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Akohoue F., and Miedaner T., 2022, Meta-analysis and co-expression analysis revealed stable QTL and candidate genes conferring resistances to Fusarium and Gibberella ear rots while reducing mycotoxin contamination in maize, Frontiers in Plant Science, 13(2022): 1050891. https://doi.org/10.3389/fpls.2022.1050891 Chen Z.J., Tang D.G., Ni J., Li P., Wang L., Zhou J., Li C., Lan H., Li L.J., and Liu J., 2021, Development of genic KASP SNP markers from RNA-Seq data for map-based cloning and marker-assisted selection in maize, BMC Plant Biology, 21: 1-11. https://doi.org/10.1186/s12870-021-02932-8 Cui Y.R., Li R.D., Li G.W., Zhang F., Zhu T.T,, Zhang Q.F., Ali J., Li Z.K., and Xu S.Z., 2019, Hybrid breeding of rice via genomic selection, Plant Biotechnology Journal, 18(1): 57-67. https://doi.org/10.1111/pbi.13170
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