Maize Genomics and Genetics 2024, Vol.15, No.1, 18-26 http://cropscipublisher.com/index.php/mgg 20 individuals with different phenotypes (e.g., corn varieties with different nutritional quality traits), genetic variants that are significantly associated with specific traits can be identified. 2.2 Methods of applying GWAS in corn genetic research In corn genetic research, GWAS methods have been widely used to analyze the genetic basis of complex traits, such as yield, disease resistance, and nutritional quality (Dudley et al., 2007). Through GWAS, researchers can quickly identify key genes or genetic markers related to target traits in a large amount of genetic resources in maize (Figure 1) (Dudley et al., 2007). For example, in research on improving the nutritional quality of corn, GWAS can help scientists discover genetic loci that affect protein content, oil content, and vitamin content. This information not only helps to understand the genetic mechanism of traits, but also provides important molecular markers for molecular-assisted breeding of corn, thereby accelerating the selection of corn varieties with high nutritional value. Figure 1 Distribution of genome-wide significant SNPs, QTL intervals, and related genomic features (Dudley et al., 2007) 2.3 GWAS data analysis and interpretation The analysis and interpretation of GWAS data is a complex process involving a large amount of bioinformatics analysis. First, it is necessary to perform quality control on the large number of SNPs data generated by GWAS to exclude markers with unclear genetic information or high missing rates. Afterwards, statistical analysis methods, such as linear mixed models, are used to evaluate the strength of the association between each SNPs and the trait phenotype. Ultimately, significantly associated SNPs markers are further studied to determine whether they are
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