Maize Genomics and Genetics 2024, Vol.15, No.1, 9-17 http://cropscipublisher.com/index.php/mgg 12 environmental factors. For example, changes in environmental factors such as temperature and humidity can affect gene expression patterns, thereby affecting trait performance. In addition, through modern genetic methods such as genome-wide association analysis (GWAS), scientists have also discovered many previously unknown new genes and genetic loci that affect corn quality traits. These new discoveries provide us with more comprehensive genetic resources. For understanding and improving corn quality. Research in recent years has revealed a series of complex genetic networks involving multiple biological processes and metabolic pathways that together determine the final content of corn starch, protein, and oil. For example, in the process of starch synthesis, in addition to the known starch synthesis-related genes, the roles of some transcription factors and signaling molecules have also been gradually revealed. These factors affect the synthesis of starch by finely regulating the expression and activity of starch synthase. and accumulation. In the regulation of protein and lipid metabolism, in addition to key synthase genes, transporters, degradative enzymes, and components in the fatty acid oxidation pathway also have an important impact on the formation of these traits. Significant progress has been made in research on the genetic basis of maize quality traits (Chaudhary et al., 2016). By deeply exploring the key genetic factors and their regulatory networks that influence starch, protein and oil content, we can not only improve the nutritional value and processing properties of corn, but also contribute to global food security and sustainable development. In the future, with the continuous advancement of genomics, epigenetics and molecular breeding technology, the ability to accurately regulate corn quality traits will be further enhanced, providing people with richer and healthier corn products. 3 Application Cases of GWAS Research in Corn Quality Improvement Genome-wide association analysis (GWAS), as an efficient genetic research tool, has shown great potential in the field of corn quality improvement. Through GWAS, scientists are able to identify genetic loci associated with specific traits on a genome-wide scale, which provides strong support for a deep understanding of the genetic basis of corn quality traits. The following are several application cases of using GWAS in corn quality improvement. 3.1 Case study 1: genetic loci affecting starch content discovered through GWAS In a typical GWAS research case, scientists focused on the genetic regulation mechanism of corn starch content (Guo et al., 2023) (Table 1). Through genome-wide scanning of a large number of corn varieties, the study found several significantly related genetic loci, which are closely related to genes for key enzymes in the starch synthesis pathway. Among them, a genetic marker located on the fifth chromosome was significantly correlated with the expression level of the starch branching enzyme (SBE) gene, which is directly involved in the synthesis of starch. This discovery not only reveals the key genetic factors affecting starch content, but also provides the possibility to improve corn starch content through molecular breeding. Table 1 Statistical analysis of corn quality traits under different environments (Chinthiya et al., 2019) Trait Environment CV(%) Mean±SD Variance Kurtosis Skewness H2 (%) Protein 2015LY 8.13 11.01±1.088 0.878 0.221 -0.368 82.73 2015QZ 9.88 11.87±1.080 1.184 0.36 0.096 2016JZ 9.10 4.54±0.432 1.166 0.144 -0.18 2017JZ 8.87 4.35±0.546 1.011 0.325 0.588 Starch 2015LY 1.87 11.34±1.006 1.733 -0.28 0.136 85.82 2015QZ 1.64 70.46±1.316 1.373 -0.518 0.731 2016JZ 1.60 71.54±1.172 1.262 0.3 0.857 2017JZ 1.58 70.24±1.124 1.247 0.974 -0.22 Oil 2015LY 9.52 70.63±1.117 0.186 0.372 0.734 80.69 2015QZ 12.55 4.47±0.572 0.298 0.298 0.871 2016JZ 12.79 4.70±0.558 0.327 0.129 0.161 2017JZ 11.87 11.01±1.088 0.334 0.621 0.851
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