Rice Genomics and Genetics 2024, Vol.15, No.2, 48-57 http://cropscipublisher.com/index.php/rgg 56 further strengthened (Wang et al., 2015). 6Outlook GWAS has become a key technology in the field of rice genetic breeding, and its role in revealing the genetic basis of complex traits and accelerating the process of genetic improvement has become increasingly prominent. In the future, the application of GWAS is expected to be further expanded and deepened, especially when combined with the latest genome editing technologies, such as the CRISPR/Cas system, to achieve precise improvement of specific traits. In addition, with the improvement of computing power and advancement of statistical methods, GWAS can process larger data sets and improve the accuracy and efficiency of association analysis. In rice breeding, GWAS is expected to promote the transformation from the improvement of single traits to the improvement of comprehensive traits in multiple traits and environments, providing strong support for cultivating rice varieties that are adaptable to climate change and have high and stable yields. Understanding of the rice genome is critical to uncovering its complex genetic and phenotypic diversity. With the development of high-throughput sequencing technology and the reduction of costs, the comprehensive analysis of the rice genome will be more in-depth, including the study of non-coding regions, epigenetic modifications, and interactions between genes. This not only helps to discover new functional genes and regulatory elements, but also reveals the regulatory mechanism of gene expression and the impact of genetic variation on complex traits. In addition, a comprehensive understanding of the rice genome will promote the development of precision breeding technology, achieve efficient utilization of rice genetic resources, and provide a solid foundation for the continuous improvement of rice yield and quality. The main challenges facing rice genetic breeding include adapting to climate change, improving disease resistance and meeting growing food demand. Faced with these challenges, modern genetic techniques such as GWAS provide new opportunities. Through GWAS, breeders can more accurately identify genetic variations associated with key traits, accelerating the development of resistant varieties and improvement of yield traits. In addition, combining genomic selection (GS) and predictive breeding, trait improvement can be carried out in a wider range of genetic backgrounds, improving the efficiency and adaptability of breeding. At the technical level, the application of artificial intelligence and machine learning will further optimize the analysis process and accuracy of GWAS, and promote the development of rice genetic breeding in a more precise and efficient direction. GWAS and its combination with other modern biotechnologies will open up new paths for rice genetics and breeding, facing both challenges and huge opportunities. Future research needs to continue to explore innovative breeding strategies and technologies based on an in-depth understanding of the rice genome to achieve dual improvements in rice yield and quality and meet the needs of global food security. Acknowledgments The author extends sincere thanks to two anonymous peer reviewers for their invaluable feedback on the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bauchet G., Grenier S., and Samson N., 2017a, Identification of major loci and genomic regions controlling acid and volatile content in tomato fruit: implications for flavor improvement , New Phytologist, 215(1): 48. https://doi.org/10.1111/nph.14615 Bauchet G., Grenier S., and Samson N., 2017b, Use of modern tomato breeding germplasm for deciphering the genetic control of agronomical traits by genome wide association study, Theoretical and Applied Genetics, 130(5): 875-889. https://doi.org/10.1007/s00122-017-2857-9 Chopra R., Burow G., and Burke J.J., 2017. Genome-wide as-sociation analysis of seedling traits in diverse Sorghum germplasm under thermal stress, BMC Plant Biology, 17(1): 12. https://doi.org/10.1186/s12870-016-0966-2 Fang L., Wang Q., and Hu Y., 2017, Genomic analyses in cotton identify signatures of selection and loci associated with fiber quality and yield traits, Nature
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