Legume Genomics and Genetics 2025, Vol.16, No.1, 11-22 http://cropscipublisher.com/index.php/lgg 14 Figure 2 Computational speed and statistical power improvement of genome-wide association study methods (Adapted from Cortes et al., 2021) Image caption: This figure displays the performance of various genome-wide association study (GWAS) methods in terms of improvements in computational speed and statistical power. The methods are categorized into several types, including multi-locus models, strategies for reducing the K matrix, and mixed-model frameworks that control for population structure (Q) and kinship (K). Among them, multi-locus models like FarmCPU and MLMM show higher statistical power, while mixed-model-based strategies such as CMLM and ECMLM demonstrate improvements in computational speed (Adapted from Cortes et al., 2021) 4 GWAS of Soybean Drought Resistance Traits 4.1 Mapping of drought-related QTLs and candidate gene discovery GWAS has been a great help in researching the drought resistance of soybeans. There was an interesting study in 2020 (Wang et al., 2020), which found 75 QTLs affecting plant weight and 64 QTLs affecting plant height in cultivated soybeans in China. These loci together can explain more than half of the phenotypic variation. Interestingly, drought resistance is not determined by a single gene, but rather by the collective action of a large number of QTL allele genes. Later in 2023, further research was conducted (Li et al., 2023), and 39 yield related SNP loci were identified under both normal watering and drought conditions, distributed across 26 genomic regions. Of particular note are genes Glyma.19G211300 and Glyma.17G057100, which are directly involved in drought stress response. These findings have further deepened people's understanding of the drought resistance mechanism of soybeans. When it comes to the localization of drought resistant genes in soybeans, there have been many new discoveries in recent research. In 2022, there was an interesting study (Sun et al., 2022) that combined GWAS with linkage analysis and found 11 SNPs and 22 QTLs in one go. Of particular note is the qGI10-1 locus, which has been validated through both methods, and it has been found that the GmNFYB17 gene not only regulates drought resistance but also affects root growth. However, when it comes to gene function, another study using RIL population is more detailed (Ouyang et al., 2022), where they identified 5 drought resistant QTLs and identified 9 candidate genes, including some NAC transport factors. These proteins may play a key role in drought resistance, although the specific mechanism still needs further research. 4.2 Gene function analysis and drought regulatory networks Bioinformatics analysis can be of great help in understanding how these drought resistant genes work specifically. Taking the 2020 study by Wang et al. (2020) as an example, candidate genes in Chinese cultivated soybeans can be classified into nine major categories, with ABA and stress response factors accounting for the majority. This indicates that they are particularly important in drought resistance. However, in terms of practical applications, the GmNFYB17 gene is more interesting (Sun et al., 2022). After overexpressing it in soybeans, not only does it enhance drought resistance, but it also increases yield. Especially under water deficient conditions, these genetically modified plants have better root growth and significantly improved yield related traits, although further research is needed on how to achieve this.
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