Legume Genomics and Genetics 2025, Vol.16, No.1, 44-53 http://cropscipublisher.com/index.php/lgg 51 quickly figure out the genetic code of drought and heat resistance (Joukhadar et al., 2021). Although these new technologies are not easy to use, if we can truly understand them, breeding will be much easier in the future. Ultimately, no matter how advanced the research methods are, it still depends on whether higher-quality and higher-yield soybeans can be grown. 7 Concluding Remarks The research on soybean genes may be going to play some new tricks next. Those algorithms in machine learning are quite popular now, such as support vector regression (SVR) and random forest (RF). It is indeed more accurate and faster to find QTL in GWAS. Sequencing technology is also becoming increasingly advanced. It is estimated that in the future, when analyzing the soybean genome, we will be able to see it more thoroughly. However, the most urgent matter might still be the study of climate adaptability - after all, the weather is getting more and more strange now, and it is necessary to quickly figure out the genetic code of drought and heat resistance. Although these new technologies are not easy to use, if we can truly understand them, breeding will be much easier in the future. Ultimately, no matter how advanced the research methods are, it still depends on whether higher-quality and higher-yield soybeans can be grown. When it comes to soybean breeding, it's actually quite interesting. Previously, people might not have paid much attention to it, but recently some particularly crucial genetic loci have been discovered (both MAS and GS technologies are applicable). However, to be fair, merely finding the site is not enough; it also depends on how it is actually used. Take seed yield and weight for example. Some haplotypes can remain stable in different environments, which is indeed helpful for cultivating more adaptable soybeans. Now, what's even more impressive is that machine learning has also been incorporated, making GWAS analysis more accurate and it's much easier to find QTLS. Although technology is becoming increasingly advanced, in the final analysis, it is still for one goal: to make breeding more efficient, to grow soybeans better and with higher yields (this is indeed quite important for the sustainability of global soybean cultivation). However, it must be admitted that various unexpected situations still occur in actual operation. After all, agriculture has never been smooth sailing. How will GWAS research on soybeans develop in the future? To be honest, it's really hard to say. Although genotyping technology is constantly advancing (and machine learning methods are becoming increasingly useful), there are still many variables in practical application. The integration of multi-environment data can indeed enhance the accuracy of analysis. However, given the significant differences in climate and soil among various regions, it remains questionable whether the results can be universally applied. Interestingly, it has recently been discovered that there may be certain interactions between different loci that we have not yet fully understood, and these may have a more significant impact on agronomic traits than individual loci. Of course, collaborative research and meta-analysis are indispensable. After all, it is necessary to verify the reliability of these findings in different environments (otherwise, breeders would not dare to use them casually). Overall, the outlook is quite optimistic, but to fully predict the genetic structure of complex traits, it may still take some time to explore. Acknowledgments CropSci Publisher sincerely thanks the two anonymous peer reviewers for their suggestions. 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 Abdelraheem A., Thyssen G., Fang D., Jenkins J., McCarty J., Wedegaertner T., and Zhang J., 2020, GWAS reveals consistent QTL for drought and salt tolerance in a MAGIC population of 550 lines derived from intermating of 11 upland cotton (Gossypium hirsutum) parents, Molecular Genetics and Genomics, 296: 119-129. https://doi.org/10.1007/s00438-020-01733-2 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: 104. https://doi.org/10.1007/s00425-020-03499-8
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