! ! Legume Genomics and Genetics 2025, Vol.16, No.1, 1-10 http://cropscipublisher.com/index.php/lgg! ! 4! 4.2 Phenotypic and environmental variability Some important traits of soybeans, such as yield per unit area, plant height and nutrient content in seeds, are often affected by the environment. The same variety may perform very differently when grown in different places. This will make GWAS more difficult to conduct, as sometimes we cannot find those gene markers that remain stable in various environments. For instance, a study tested the same batch of soybean varieties in four places in southern Brazil. It was found that although some SNPS could recur at multiple locations, most markers only exhibited character-related relationships in certain specific environments (Contreras-Soto et al., 2017). This indicates that to obtain more reliable results, it is necessary to verify multiple times in different environments and use more rigorous statistical methods. 4.3 Complex traits and epistasis Many important traits of soybeans, such as disease resistance, maturity time and yield, are controlled by multiple genes together. Such traits are called "complex traits". Moreover, these genes can also influence each other, and this phenomenon is called "superiority". The traditional GWAS method analyzes each SNP one by one. This approach makes it difficult to identify the interactions between genes, and many complex genetic relationships may be missed. Now, researchers have begun to use some new methods to solve this problem. For instance, GWAS based on haplotypes can analyze multiple adjacent mutation points at once, and sometimes even find results that traditional methods cannot. In addition, some subsequent analyses have also begun to integrate more types of data, such as gene expression, protein interaction information, etc. These practices can help us understand more comprehensively how traits are regulated and also clearly see how different genes interact with each other (Mortezaei and Tavallaei, 2021). However, these new methods are still not mature enough at present, especially when analyzing the issues of "one gene influencing multiple traits" (pleiotropy) and "the interaction of multiple genes", there are still many technical challenges to be solved. 5 Case Study: GWAS in Soybean Disease Resistance 5.1 Background and objectives Soybean (Glycine max) is one of the most widely grown food crops in the world and also an important cash crop. However, during the planting process, soybeans are often infected by various bacteria. These diseases will lead to a decrease in output and a deterioration in quality. To reduce these losses, an effective approach is to take advantage of the inherent disease resistance of soybeans. Nowadays, many scientists use a method called GWAS (Genome-wide Association Study) to identify key gene regions related to disease resistance. This section mainly introduces some achievements made by GWAS in the study of several common soybean diseases. These diseases include leaf spot disease caused by Cardamom, brown rust disease resulting from cardamom, and sclerotinia (SSR), which can cause the stems of soybeans to rot. Through these studies, scientists hope to figure out the genetic mechanism of soybeans' disease resistance. They also hope that these discoveries can provide useful assistance for future breeding. 5.2 Methodology and findings Nowadays, GWAS has been widely used to study the disease resistance of soybeans. For instance, there was a study that analyzed 246 samples of soybean materials using the SNP50K gene chip, with the target being Corynebacterium carinii. This study identified 14 related SNPS and 33 loci, which were associated with the resistance of the two strains. Six loci were consistent in both strains. In addition, researchers also combined RNA-Seq technology to analyze the changes in gene expression and identified a total of 238 genes that were significantly altered in the disease resistance response. These genes may be involved in the immune mechanism of soybeans (Figure 1). Another study specifically focused on soybean brown rust (SBR), a disease caused by a pathogen called cardamom. Researchers analyzed 3 082 samples of soybean materials and identified many significant SNPS related to disease resistance (Table 1). Some SNP loci are located near known disease-resistant genes, such as Rpp1, Rpp2, Rpp3 and Rpp4. Some new locations were also discovered in the research, which might be related to
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