Legume Genomics and Genetics 2025, Vol.16, No.1, 11-22 http://cropscipublisher.com/index.php/lgg 20 Research on drought resistance of soybeans can now be regarded as entering the "big data era" (Bao et al., 2015). Imagine throwing genetic data, field phenotypic data and meteorological, soil and environmental data all into an intelligent analysis platform, allowing machine learning algorithms to find patterns on their own-this is much faster than manual analysis in the past. Especially for those key genes with pleiotropy, traditional methods often miss them. Now, through multi-dimensional association analysis, none of them can escape. But to be honest, building such a platform is no easy task: first of all, the issue of data standardization must be addressed, after all, the data collection methods in different test fields are all over the place. Secondly, the algorithm must be proficient enough to distinguish between genuine gene-environment interactions and merely data noise. As early as 2015, teams attempted to develop such tools. Although they were still in the refinement stage, they could already help breeders identify drought-resistant candidate genes more quickly. If in the future these intelligent prediction systems can be directly connected to the breeding process, from gene discovery to variety selection and breeding can be completed in one go, that would be truly exciting! 8 Concluding Remarks Research on the drought resistance of soybeans has made breakthrough progress in recent years-and GWAS deserves much credit for this. In the past, finding drought-resistant genes was like looking for a needle in a haystack. Now, with high-density SNP markers, it's just like having opened the "Heavenly Eye". Look, just for controlling yield and plant height, dozens of QTLS have been found, not to mention those key sites that affect root development. It is particularly worth mentioning the star gene GmNFYB17. It is truly amazing. It can not only help soybeans resist drought but also increase their yield. It is simply the dream "cause" for breeders. But then again, drought resistance is too complex to be solved by just one or two genes. It's like a jigsaw puzzle, and now there are finally a few key pieces. Although the complete genetic map is still being drawn, these discoveries have already saved drought-resistant breeding from many detours. At least now we know in which direction to focus our efforts. GWAS has become increasingly proficient in the research on drought resistance of soybeans and is sure to achieve great success in the future. Now, with those high-tech means-like GBS sequencing, SNP chips and the like-finding drought-resistant genes is like having a cheat code. When these technologies work together, they can identify all the drought-resistant alleles hidden in soybean germplasm resources, and perhaps even discover some new variations that have never been seen before. Even more amazing is the use of meta-analysis, which packages and processes the data from various studies. The statistical power is directly maximized, and the reliability of the QTLS found also increases rapidly. In this way, marker-assisted selection becomes much more precise, and breeders no longer have to rely on luck as they did before. Although it is still impossible to achieve 100% accuracy at present, if we continue along this path, it will surely become easier and easier to cultivate drought-resistant soybean varieties. Nowadays, in soybean drought resistant breeding, GWAS, GS, and gene editing are advancing together, and the effect is really 1+1+1>3. GWAS first acts as a scout, identifying drought resistance related QTLs and candidate genes one by one; Then GS comes on stage and uses these labeled data to build a prediction model, which can predict which plants are more drought resistant during the seedling stage; Finally, CRISPR emerged and edited target genes like a precision surgical knife. The most wonderful thing about this combination boxing style is that it retains the advantages of traditional breeding while incorporating precise molecular level operations. For example, the GmNFYB17 gene was first discovered by GWAS, and then gene edited to enhance expression. Finally, the GS model proved that it can indeed improve yield stability under drought conditions. Although this process is still being optimized, it has already shortened the breeding cycle for drought resistant varieties by several years. As climate change becomes increasingly challenging, this multi technology integration strategy may be the key to ensuring soybean production in the future. Acknowledgments Thanks to the anonymous peer review for providing targeted revision suggestions for the manuscript.
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