Maize Genomics and Genetics 2024, Vol.15, No.1, 18-26 http://cropscipublisher.com/index.php/mgg 21 located near or within known functional genes that may directly or indirectly regulate the target trait. In addition, the interpretation of GWAS results also needs to consider the influence of population structure and genetic background to ensure the authenticity of the association results. GWAS provides an efficient method for genetic research in crops such as corn, and can reveal the genetic basis of traits at the genome-wide level (Edwards wt al., 1987). Through GWAS, we can not only discover the key genetic factors that affect the nutritional quality traits of corn (Table 1) (Chinthiya et al., 2019), but also use this knowledge to guide corn breeding (Liu, 2002), thereby breeding more adaptable Excellent variety for human needs. However, the analysis and interpretation of GWAS data requires complex statistical and bioinformatics methods, which requires researchers to have interdisciplinary knowledge and skills. With the continuous improvement of analysis tools and the improvement of computing power, the application of GWAS in genetic research of corn and other crops will be more extensive and in-depth in the future. Table 1 Analysis of variance of parents and sweet corn hybrids for various biometrical and quality characters Source of variance Df DFF D50%T D50%S ASI DM CPH PH CL Replication 1 0.1184 0.1184 0.18 1.0658 0.0202 313.229 11.9688 2.9962 Treatment 36 8.5366** 5.7799** 3.42** 2.6447** 19.7650** 356.25** 707.5577** 12.0902** Error 36 1.9563 0.4911 0.6657 0.4712 7.9232 130.4861 206.7662 1.5691 Note: **: Significant at 0.01 level; *: Significant at 0.05 level; DFF: Days to first flowering; D50%T: Days to 50% tasseling; D50%S: Days to 50% silking; ASI: Anthesis silking interval; DM: Days to maturity; CPH: Cob placement height; PH: Plant height (Chinthiya et al., 2019) 3 Application of GWAS in the Study of Corn Nutritional Quality Traits With the rapid development of molecular biology and genomics, genome-wide association studies (GWAS) have become a powerful tool to explore the genetic basis of crop nutritional quality traits. In the study of corn nutritional quality traits, GWAS methods have made a series of important discoveries. These results not only enrich our understanding of corn genetic diversity, but also provide scientific basis for the nutritional improvement of corn. 3.1 Important findings Through GWAS methods, scientists have successfully identified multiple key genetic loci and genes related to corn nutritional quality traits. For example, when studying the genetic basis of corn oil content, researchers found that several SNPs located in specific regions of the corn genome were significantly related to oil content. The genes in these regions may be directly involved in the synthesis and regulation of oil in corn kernels (Liu et al., 2008). In terms of protein content, GWAS analysis also revealed some key genetic markers. The genes near these marker sites are involved in key pathways of nitrogen absorption and metabolism. In addition to the above traits, GWAS has also been used to explore the genetic regulation mechanism of vitamin and mineral content in corn. For example, some studies have discovered genetic loci related to vitamin E content in corn through GWAS methods, which provides clues for further research on key genes in the vitamin E synthesis pathway. These findings not only enhance our understanding of the genetic basis of maize nutritional quality traits, but also provide the possibility for improving the nutritional quality of maize through molecular breeding methods in the future. 3.2 Case study Taking a GWAS study on the genetic basis of corn protein content as an example, the research design included genotypic sequencing of thousands of corn varieties and phenotypic determination of protein content. By analyzing these data, the researchers successfully identified multiple SNPs that were significantly associated with protein content. Further gene annotation and functional analysis revealed that some genes near these SNPs are involved in nitrogen absorption and metabolism processes, which have a direct impact on the formation of corn protein content. Another case is a GWAS study on the vitamin content of corn. The research design is also based on large-scale genotype and phenotype data sets. Through sophisticated statistical analysis, the research team discovered several
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