Legume Genomics and Genetics 2025, Vol.16, No.1, 44-53 http://cropscipublisher.com/index.php/lgg 48 deviation in the analysis results. However, there are now solutions, such as using PCA analysis or kinship matrix for correction (Zhang et al., 2015), and the recently released FarmCPU model (Priyanatha et al., 2022) is also quite good at handling such problems. Another common pitfall is false positives, where unreliable results keep popping up. At this point, the significance threshold needs to be adjusted more strictly, or re-validated in another population (Sonah et al., 2015). If conditions permit, double confirmation of QTL localization should also be conducted (Kim et al., 2022). Although there are many problems, as long as all these pitfalls are eliminated, GWAS can still help us dig out a lot of useful genetic information, which is of great help to breeding work. To put it bluntly, no matter how good a tool is, it depends on how people use it. The key is to do every link well. 4 Genetic Loci Underpinning Yield, Stress Tolerance, Nutritional Value, and Disease Resistance in Soybean 4.1 Loci associated with yield-related traits GWAS has indeed been of great help in studying the size of soybean seeds and the number of pods. The genome was scanned using GBS technology and the results were quite interesting - 1 to 8 key loci were discovered. These sites not only controlled seed weight and plant height (Sonah et al., 2015), but also overlapped with the previously reported QTL regions, indicating that the right location was found. Even better, the same method also identified the gene loci that affect the flowering time and maturity period. To ensure there were no mistakes, the researchers deliberately used two-parent hybrid populations for verification, and the results showed that these loci were indeed reliable. However, to be fair, although these key loci have been identified, how exactly these traits are regulated may still require further exploration. 4.2 Loci linked to abiotic stress tolerance Although there is currently a lack of direct GWAS evidence of drought resistance in soybeans, research on other crops may provide clues. For instance, the rice field is quite interesting. Someone identified 82 meta-Qtls related to drought resistance through meta-analysis (Yang et al., 2020). This research approach might be applicable to soybeans. When it comes to stress resistance, there are some findings in soybean salt resistance studies themselves - there is a major locus on chromosome 3 that is particularly prominent (Do et al., 2019), but there are also some genes on chromosomes 1, 8 and 18 that affect the salt stress response, such as those that control the degree of leaf damage and chlorophyll content. These results indicate that salt resistance is a rather complex issue that cannot be resolved by a single gene, but at least it points out a clear path for molecular marker-assisted breeding. If the research on salt resistance and drought resistance can be combined, perhaps some common stress resistance mechanisms can be discovered, which should be helpful for breeding new varieties that adapt to climate change. 4.3 Loci associated with nutritional traits The GBS-GWAS technology has indeed found a treasure in improving the quality of soybeans. When analyzing the seed protein content, researchers not only identified key sites (Sonah et al., 2015), but more excitingly, these sites were highly consistent with the previously reported QTL regions - indicating that we may have grasped the core genes that determine the protein content. Coincidentally, a similar pattern was also discovered when analyzing the fat content (Wang et al., 2020). These overlapping genetic loci are simply natural quality control switches. However, interestingly, although the genetic loci for both proteins and fats have been identified, in actual breeding, one still needs to pay attention to the possible waxing and waning relationship between them. After all, from a molecular perspective, the metabolic pathways of these two traits are very likely to influence each other. The most practical value of these findings lies in the fact that in the future, when cultivating high-protein or high-oil soybean varieties, we can directly target these key sites for precise selection and breeding. 4.4 Loci contributing to disease resistance The GBS-GWAS method is also quite reliable for identifying disease-resistant genes. Although the data at hand does not specifically state where the resistance sites of soybean cyst nematodes are, following this train of thought is definitely correct. Just think about it, as long as the markers of the whole genome are densely dotted and the disease phenotypic data are carefully compared (Abdelraheem et al., 2020), even a tough bone like Phytophthora
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