! ! Legume Genomics and Genetics 2025, Vol.16, No.1, 1-10 http://cropscipublisher.com/index.php/lgg! ! 5! new disease-resistant genes. These SNPS also performed well in genomic prediction, with a higher accuracy rate than before (Xiong et al., 2023). In the study of sclerotinosis (SSR), a GWAS combined with an upper-level analysis based on 466 soybean samples identified 58 major loci and 24 groups of gene interaction signals. All of these are related to disease resistance. The research also identified some candidate genes, which may be involved in processes such as cell wall regulation, hormone conduction, and sugar distribution. This also indicates that the resistance mechanism of SSR is rather complex (Moellers et al., 2017). Figure 1 Go term analysis conducted on upregulated genes within resistance genotypes yielded plots illustrating (Adopted from Patel et al., 2023) Image caption: (A) Biological processes for Bedford (B) Biological processes for Council, (C) Molecular processes for Council, and (D) Represent significantly enriched KEGG pathways identified for differentially expressed genes (DEGs) that were shared across all four genotypes (Adopted from Patel et al., 2023) 5.3 Implications for breeding The research results of these GWAS are very helpful for soybean breeding. As long as SNPS or genes related to disease resistance are identified, breeders can use them for marker-assisted selection (MAS) or genomic selection (GS). In this way, the breeding efficiency can be significantly improved (Huang, 2024). For instance, when studying Corynebacterium carinii, scientists analyzed the data from GWAS and RNA-Seq together. They used these two methods together to study the disease resistance traits of soybeans. This approach enabled them to have a more comprehensive understanding of the genetic basis of soybeans and also provided a clear direction for breeding work (Patel et al., 2023). For instance, when studying soybean brown rust (SBR), scientists discovered some SNPS that were close to known disease-resistant genes. Breeders can use these SNPS to screen out resistant materials more quickly and accurately. Sclerotinia (SSR) is even more complicated. Its genetic structure is rather unique, so scientists use GWAS in combination with upper-level analysis to study it. Not only did they identify the key loci, but they also discovered many interactions between genes. These newly discovered candidate genes can also serve as key targets for subsequent breeding. They may help breed new soybean varieties that are resistant to multiple diseases and have stable resistance.
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