Legume Genomics and Genetics 2025, Vol.16, No.1, 44-53 http://cropscipublisher.com/index.php/lgg 49 root rot can find resistance sites (Shook et al., 2021). To put it bluntly, the key lies in two points: one is that there should be a sufficient number of SNP markers, and the other is that QTL positioning should be used for further verification. With such a combination of measures in place, no matter how deeply hidden the disease-resistant genes are, they can be exposed. In fact, it's not just disease resistance. The approach of GWAS combined with QTL mapping has proven effective time and again in soybean research. From yield to stress resistance, from nutritional quality to disease resistance, almost all the important sites that need to be identified have been thoroughly investigated. The most practical use of these discoveries is that they have drawn a precise "treasure map" for molecular breeding - in the future, if you want to improve a certain trait, you can simply follow the map, avoiding the need to search for a needle in a haystack in the vast genome. 5 Applications of GWAS Results in Soybean Breeding 5.1 Marker-assisted selection (MAS) using identified loci When it comes to soybean breeding nowadays, it is getting more and more precise. Remember that in the past, seed selection relied entirely on experience. Now, with the technology of marker-assisted selection (MAS), one can directly look at the genetic markers - such as those loci that control protein and lipid content (Sonah et al., 2015; Huang, 2024). In fact, GWAS has already helped us find many useful markers, not only seed components, but also traits such as plant height and seed size have corresponding loci (Shook et al., 2021). Although there are still some troubles in actual operation, applying these markers to MAS is indeed effective. At least it can help breeding work avoid detours and the speed of new varieties coming out is much faster. However, to be fair, having marks alone is not enough. In the end, it also depends on whether the field performance can truly achieve the expected results. 5.2 Genomic selection (GS) and its integration with GWAS Nowadays, there is an interesting technique in soybean breeding called genomic selection (GS). To put it simply, it involves scanning the entire genome to predict which soybeans are more worthy of cultivation. However, using GS alone might not be accurate enough, so the researchers came up with a brilliant idea - adding the important loci found in GWAS. For instance, some people incorporated SNP markers that control protein content into the GS model (Qin et al., 2022), and as a result, the efficiency of selecting high-protein varieties increased significantly. Interestingly, this combination of measures is much more reliable than randomly selecting some genetic markers, indicating that the combination of GWAS and GS technologies can indeed produce an effect greater than the sum of its parts. Although various parameters still need to be debugged in the actual operation, at least it is now known that this approach is feasible. In the future, when cultivating new varieties, we should be able to avoid some detours. 5.3 Development of climate-resilient varieties It's getting harder and harder to grow soybeans nowadays - the weather is either too dry or too hot, which makes the yield very unstable. Fortunately, GWAS technology has helped us identify some key genes, such as those loci that control flowering time (Kim et al., 2022), and these findings might come in handy. Although it still needs to be explored exactly how to use them, applying these stress resistance gene markers to breeding can at least increase the chances of new varieties winning in bad weather (Ravelombola et al., 2021). Ultimately, nowadays, in breeding, it is not only necessary to pursue high yields, but also to find ways to enable soybeans to withstand increasingly abnormal weather conditions; otherwise, even having enough to eat will be a problem in the future. 5.4 Enhancing nutritional quality through identified genes When it comes to soybeans, in the final analysis, it all depends on their nutritional value - after all, so many people around the world rely on them to supplement protein and oil. In recent years, GWAS studies have indeed unearthed many valuable genes, such as those sites that directly affect protein content and amino acid composition (Shook et al., 2021), and new discoveries were added last year (Yoosefzadeh-Najafabadi et al., 2023). However, interestingly, although so many key loci have been identified, it still depends on how these genes are combined to
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