Legume Genomics and Genetics 2025, Vol.16, No.1, 11-22 http://cropscipublisher.com/index.php/lgg 13 associated with specific traits. However, the actual operation is not that simple. Just collecting enough samples for genotyping is enough to make a fuss (Korte and Farlow, 2013). Fortunately, with the help of molecular markers such as SNPs, statistical analysis has become much more convenient. In crops like soybeans, key loci controlling complex traits were identified using GWAS in 2016 (Chang et al., 2016). But on the other hand, this method also depends on the specific situation. A paper in 2021 pointed out that the results obtained under different experimental conditions may have significant differences (Cortes et al., 2021). Figure 1 Analysis of root growth and root-to-shoot ratio in transgenic soybean (Adapted from Sun et al., 2020) Image caption: A: Morphological differences in the roots of transgenic soybean lines G16, G18, G26, and wild-type (WT) soybean after a 6-week growth period; B: Differences in root length between each line and WT; C: Number of lateral roots within a 10 cm root segment; D: Comparison of root-to-shoot ratio differences between transgenic lines and WT; Statistical data indicate that transgenic lines G16, G18, and G26 exhibit significantly greater root length, lateral root number, and root-to-shoot ratio compared to WT, with these differences showing statistical significance across multiple transgenic lines (*: p ≤ 0.05; **: p ≤ 0.01) (Adapted from Sun et al., 2020) 3.2 Current applications of GWAS in soybean research Nowadays, GWAS is widely used in soybean research, especially in identifying those gene loci related to drought resistance. It is quite interesting to remember that there was a study in 2023. They discovered some SNP markers, which were associated with yield traits such as pod number and biomass (Li et al., 2023), and could be detected under both normal watering and drought conditions. In fact, as early as 2015, researchers used the GBS method for genome-wide labeling (Sonah et al., 2015), and extracted the gene loci of traits such as maturity period and plant height. But the most impressive one was the later analysis that combined 73 studies (Shook et al., 2020), which put data from different sources together, and the accuracy of finding drought-resistant genes improved all at once. These studies have indeed deepened our understanding of the drought resistance of soybeans significantly. 3.3 GWAS data analysis tools and methods When it comes to the data analysis of GWAS, there are actually quite a few methods, each with its own uses. Look at that hybrid model framework, which is quite interesting (Figure 2) (Cortes et al., 2021). It can take into account population structure and kinship, so there can be much fewer false positive results. However, this alone is not enough. Specialized software like BLINK and rrBLUP are also quite crucial (Ravelombola et al., 2021). One is used for GWAS analysis, and the other for homosexuals etiome prediction. They are particularly useful in marker-assisted selection. If we talk about the most convenient method, it is still XP-GWAS (Yang et al., 2015). It specifically selects individuals with extreme phenotypes for sequencing and is particularly friendly to those species with limited genotyping resources. Although these tools may seem diverse, they are ultimately all aimed at better analyzing the genetic mechanism of drought resistance traits in soybeans, so that we can cultivate more drought-resistant varieties.
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