Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 4 3.1 Overview of GWAS research methods The basic process of GWAS includes sample collection, genotype sequencing, and application of statistical analysis methods (Wen et al., 2014). First, researchers collect genetically diverse populations of corn, which may come from different geographic locations or have different genetic backgrounds. Next, these samples are analyzed for genome-wide genetic variation through high-throughput genotype sequencing technology, usually focusing on single nucleotide polymorphisms (SNPs). Finally, sophisticated statistical analysis methods, such as linear mixed models, were used to identify genetic markers significantly associated with corn kernel quality traits. 3.2 Review of recent GWAS research results on corn grain quality traits In the past few years, GWAS has made a series of important discoveries in corn kernel quality traits. Researchers have successfully identified multiple genes or genetic loci that are significantly related to quality traits such as starch content, protein content, and oil content. These findings not only increase our understanding of the genetic control mechanisms of corn quality traits, but also provide valuable molecular markers for future corn variety improvement (Guo et al., 2019). The functions of genes or loci discovered through GWAS research include enzyme genes that affect the starch synthesis pathway, factors involved in protein synthesis and regulation, and key genes that regulate oil metabolism. For example, variations in certain enzyme genes directly affect the biosynthetic pathway of starch, thereby changing the starch content of grains; while variations in certain transcription factors or regulatory genes may affect key links in the protein synthesis pathway, thereby regulating grain Protein content (Guo et al., 2023). How these specific genetic variations finely regulate the quality traits of corn kernels requires further functional verification and mechanism research. 3.3 Limitations and challenges of GWAS studies Although GWAS has made remarkable achievements in revealing the genetic basis of corn grain quality traits, this method also has some limitations and challenges. GWAS requires large-scale sample data to ensure the accuracy of statistical analysis, which places higher requirements on sample collection and genotype sequencing. Due to the complexity of environmental factors and genetic background, the association between genetic markers and traits discovered by GWAS may have a certain degree of volatility and needs to be verified under different genetic backgrounds and environmental conditions. In addition, although GWAS can identify genetic loci associated with traits, further biological verification and mechanism research are needed to deeply understand the specific functions of these loci and their role in trait formation. As an efficient genetic research method, GWAS has made significant progress in the study of corn grain quality traits. Through future research, we are expected to gain a deeper understanding of the genetic mechanisms underlying the formation of corn kernel quality and use this knowledge to promote the improvement and optimization of corn varieties. 4 Molecular Mechanisms of Corn Kernel Quality Traits As one of the important food crops in the world, corn's grain quality traits directly affect the nutritional value and processing characteristics of the grain. In recent years, with the advancement of molecular biology technology, scientists have made remarkable achievements in the study of the molecular mechanisms of corn grain quality traits, especially the discovery of key genes, the analysis of gene expression regulatory mechanisms, and the application of these findings in breeding (Table 1). Table 1 Correlation coefficients between maize quality shape and main agronomic traits Project Yieldper plant 100grain weight Spike length Ear diameter Ear row number Kernels per row Height Reproductive period Crude protein -0.635* -0.789** 0.063 0.167 -0.972** 0.777* 0.09 0.766** Crude fat -0.636* 0.697* -0.184 0.160 0.160 -0.906** 0.782** 0.809** Total starch 0.555 0.135 0.799** 0.228 0.581* 0.618* 0.100 0.229 Note: *: Significant level; **: 0.01 Significant level
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