Legume Genomics and Genetics 2024, Vol.15, No.1, 13-22 http://cropscipublisher.com/index.php/lgg 14 In recent years, the application of GWAS in genetic research and breeding of leguminous crops has achieved remarkable results. For example, through GWAS analysis, researchers successfully identified multiple genetic loci in soybean (Glycine max) related to oil and protein content, disease resistance, and tolerance to drought and saline-alkali environments (Korte and Farlow, 2013). These findings not only enrich our understanding of soybean genetic diversity, but also provide valuable genetic markers for molecular-assisted selection, further promoting the progress of soybean breeding. Although GWAS has shown great advantages in revealing the potential of crop genetic traits, it also faces some challenges in practical applications, such as the genetic background of complex traits, statistical analysis problems of high-dimensional data, and the management and management of large-scale genomic data. Explanation etc. Therefore, future research needs to continue to optimize GWAS analysis methods and improve its accuracy and efficiency in analyzing complex traits. 2 Application Cases of GWAS in Leguminous Crop Research 2.1 GWAS methodology GWAS is a important technique in modern genetic research, which allows researchers to identify genetic variants associated with specific phenotypes or traits across the entire genome. In the study of leguminous crops, GWAS has become a powerful tool for discovering new genetic markers and understanding the genetic basis of traits. GWAS provide a powerful platform for identifying genetic markers associated with important traits in leguminous crops. Through the above methodological steps, GWAS can explore the genetic basis of traits across the entire genome, providing valuable resources for genetic improvement and molecular breeding of crops. The first step in GWAS is to collect a sufficient number of study samples with sufficient genetic diversity and phenotypic data on the studied traits. In the study of leguminous crops, this often means selecting a broad variety or natural group for analysis. Subsequently, the samples are genotyped using high-throughput sequencing technologies , such as single nucleotide polymorphism (SNP) chips or whole-genome sequencing , to obtain genetic marker information for the entire genome (Hoyos-Villegas et al., 2017). The collection of phenotypic data is another critical step in GWAS studies. This includes precise measurements of specific traits in leguminous crops, such as yield, disease resistance, stress tolerance, etc. The quality of phenotypic data directly affects the accuracy and reliability of GWAS analysis. Therefore, standardized measurement methods and repeated measurements across multiple environments or growing seasons are needed to ensure data stability and repeatability. With genotypic and phenotypic data in hand, the next step is to perform association analysis using statistical methods. This often involves using multiple statistical models to detect correlations between variation in genotypic data and phenotypic traits. Commonly used statistical models include linear mixed models (LMM) and fixed and random effects mixed linear models (MLM), which can effectively identify genetic markers associated with traits while controlling the effects of population structure and kinship (Wen et al., 2018). After the GWAS analysis is completed, the association signal needs to be interpreted, which includes identifying genetic markers and potential candidate genes that are significantly associated with the trait. Through further bioinformatics analysis and functional studies, such as gene expression analysis and gene function verification experiments, the mechanism of action of these candidate genes in trait formation can be deeply understood. Functional verification of candidate genes is carried out through genetic transformation, gene editing or other molecular biology techniques to confirm that these genes indeed play a key role in the trait expression of leguminous crops. This step is of crucial significance for ultimately determining the molecular basis of the trait and applying GWAS results to molecular breeding of leguminous crops.
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