! ! Legume Genomics and Genetics 2025, Vol.16, No.1, 1-10 http://cropscipublisher.com/index.php/lgg! ! 3! extreme phenotype GWAS (XP-GWAS), as well as the method of analysis using k-mer sequence features. These new technologies can more accurately identify variant sites and candidate genes related to traits (Yang et al., 2015; Lemay et al., 2023). 3 Applications of GWAS in Soybean 3.1 Identification of trait-associated loci Nowadays, many scientists use GWAS (Genome-wide Association Studies) to identify gene regions related to traits in soybeans. This method is particularly suitable for studying locations related to agronomic traits. For instance, one study utilized GBS (Genotyping sequencing) technology to identify several related regions in the soybean genome. These regions are associated with eight important traits, such as maturity time, plant height, seed size, oil content and protein content (Sonah et al., 2015). Another study combined the results of 73 independent GWAS and conducted a meta-GWAS (Meta-Analysis). A total of 393 gene regions were identified, among which 483 QTL (quantitative trait loci) were found to be related to yield, disease resistance, plant height and other traits (Shook et al., 2021). These research results indicate that GWAS is a very practical tool. It can help us identify the gene regions related to important traits more quickly and also provide many useful references for soybean breeding. 3.2 Agronomically important traits In soybeans, traits such as yield, plant height and seed weight are generally not controlled by a single gene, but are regulated by many genes together. GWAS is particularly suitable for studying such complex traits. Several SNP haplotypes have been identified in studies, and these gene loci have a significant relationship with major agronomic traits (Contreras-Soto et al., 2017). Another study employed a support vector regression (SVR) model to identify stable gene regions related to soy protein content and oil content (Yoosefzadeh-Najafabadi et al., 2023). These achievements are very useful because they enable us to have a clearer understanding from a genetic perspective which regions affect the quality of soybeans, which is conducive to increasing yield and market competitiveness (Priyanatha et al., 2022). 3.3 Contributions to breeding programs GWAS can not only identify key genes but also provide some genetic markers that can be directly used in breeding. For instance, these markers can be used in marker-assisted selection (MAS) and genomic selection (GS) to help breeders make decisions more quickly. Some studies have identified SNP markers related to yield, maturity period, plant height and seed weight, all of which can be applied in actual breeding to enhance work efficiency (Ravelombola et al., 2021). Some researchers have also analyzed soybean mutant resources using GWAS, identifying some mutation hotspots and key loci that affect agronomic traits, providing new ideas for breeding (Kim et al., 2022). Judging from these achievements, GWAS has played a significant role in the breeding of new soybean varieties. It can help us precisely pick out those plants with good traits, accelerate the breeding speed and make the improvement work more efficient. 4 Challenges and Limitations of GWAS in Soybean 4.1 Population structure and genetic diversity When conducting GWAS on soybeans, some problems are often encountered. One of the main reasons is that the genetic differences among different varieties are too great. The germplasm resources of soybeans come from many places, and the genetic backgrounds of different groups are also different. If these group differences are not taken into account during the analysis, it is very easy to achieve the result of "false association". For instance, a study analyzed approximately 14 000 soybean samples and found that soybeans from China, Japan and South Korea vary greatly in genetic composition. Most of the soybean varieties in the United States can be traced back to two subgroups in China (Bandillo et al., 2015). Therefore, when conducting GWAS, it is essential to first clarify the source of the materials and their genetic background. Only in this way can errors be reduced and the gene regions related to traits be identified more accurately.
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