! ! Legume Genomics and Genetics 2025, Vol.16, No.1, 1-10 http://cropscipublisher.com/index.php/lgg! ! 8! is known as a "meta-analysis" (meta-GWAS). A total of 393 genomic regions were identified this time, among which 483 QTLS were discovered. This indicates that when the results of multiple studies are analyzed together, more useful information can be identified. In addition to these traditional practices, nowadays some people have begun to use GWAS based on haplotypes. It is somewhat different from the method of analyzing only a single SNP and is more suitable for studying the genetic background related to complex traits. In addition, some studies have also introduced machine learning methods, such as using artificial intelligence to find QTLS. This method not only improves the accuracy of the analysis, but also can locate key genes more quickly. With the continuous development of computing technology, the role of GWAS in soybean genetic research will definitely become increasingly significant. Next, there are still many areas where the research on soybean GWAS can be further advanced: First, try more advanced statistical models. For instance, the new model 3VmrMLM not only analyzes genes but also takes environmental factors into account. This can better explain the manifestation of traits in different environments. Second, incorporate structural variation and k-mer analysis. Structural variations include gene insertions or deletions, and K-mers are some very short DNA fragments. By using these methods, some new functional regions may be discovered, and the genetic characteristics of soybeans can also be understood more comprehensively. Third, encourage more meta-GWAS to be conducted. It is to integrate and analyze the data from different teams and experiments. The result obtained in this way is more stable and reliable. Finally, and most importantly: These research results should be applied to actual breeding. Combining the results of GWAS with genomic selection (GS) or marker-assisted selection (MAS) can more quickly screen out soybean materials with high yield and strong adaptability. Overall, technology is still advancing. There is still much room for GWAS in the study of soybean genetics and the improvement of breeding efficiency. In the future, it will remain an important tool for promoting the improvement of soybean varieties. Acknowledgments The authors thank anonymous reviewers for their suggestions and comments that were useful for improving our paper’s presentation. 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 Almeida-Silva F., Moharana K., Machado F., and Venancio T., 2020, Exploring the complexity of soybean (Glycine max) transcriptional regulation using global gene co-expression networks, Planta, 252(6):104. https://doi.org/10.1007/s00425-020-03499-8 Anderson E., Ali L., Beavis W., Chen P., Clemente T., Diers B., Graef G., Grassini P., Hyten D., McHale L., Nelson R., Parrott W., Patil G., Stupar R., and Tilmon K., 2019, Soybean [Glycine max (L.) Merr.] breeding: history, improvement, production and future opportunities, Advances in Plant Breeding Strategies: Legumes, 12: 431-516. https://doi.org/10.1007/978-3-030-23400-3_12 Bandillo N., Jarquín D., Song Q., Nelson R., Cregan P., Specht J., and Lorenz A., 2015, A population structure and genome‐wide association analysis on the usda soybean germplasm collection, The Plant Genome, 8(3): 24. https://doi.org/10.3835/plantgenome2015.04.0024 Bhat J., Adeboye K., Ganie S., Barmukh R., Hu D., Varshney R., and Yu D., 2022, Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.), Frontiers in Genetics, 13: 953833. https://doi.org/10.3389/fgene.2022.953833 Contreras-Soto R., Mora F., Oliveira M., Higashi W., Scapim C., and Schuster I., 2017, A genome-wide association study for agronomic traits in soybean using SNP markers and SNP-based haplotype analysis, PLoS ONE, 12(2): e0171105. https://doi.org/10.1371/journal.pone.0171105 Cortes L., Zhang Z., and Yu J., 2021, Status and prospects of genome‐wide association studies in plants, The Plant Genome, 14(1): e20077. https://doi.org/10.1002/tpg2.20077 Huang W.Z., 2024, The current situation and future of using GWAS strategies to accelerate the improvement of crop stress resistance traits, Molecular Plant Breeding, 15(2): 52-62. http://dx.doi.org/10.5376/mpb.2024.15.0007
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