Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 5 4.1 Key genes and regulatory networks revealed The formation of corn kernel quality traits, such as starch content, protein content and oil content, is determined by a complex gene regulatory network (Liu et al., 2016). Through genome-wide association analysis (GWAS) and functional genomics research, scientists have identified multiple key genes that affect corn grain quality traits. For example, genes involved in starch biosynthesis, AGPase, GBSS, etc., play a vital role in the starch synthesis pathway. In terms of protein content regulation, the discovery of the Opaque-2 (O2) gene marks an in-depth understanding of the regulatory mechanism of corn grain protein synthesis. In terms of regulating oil content, genes such as DGAT and FAD2 are involved in the biosynthesis and metabolism of oil. These key genes and their interactions form a complex regulatory network that finely regulates the quality of corn kernels. By in-depth studying the composition and function of this regulatory network, scientists can better understand the formation mechanism of corn grain quality traits. 4.2 Gene expression regulation and its impact on quality formation The regulation of gene expression is a key link that affects the formation of corn grain quality traits (Xiong and Huang, 2022). Factors such as gene transcription level, mRNA stability and translation efficiency can affect the final protein expression, thereby affecting the quality traits of the grain. For example, the Opaque-2 (O2) gene, as a transcription factor, affects the protein content and composition of corn kernels by regulating the expression of a series of downstream genes. In addition, the influence of environmental factors on gene expression cannot be ignored. Environmental factors such as temperature, light, and soil nutritional status can indirectly affect the quality traits of corn grains by affecting gene expression patterns. Therefore, the formation of corn kernel quality traits is a complex process influenced by genetic factors and environmental factors. 4.3 Examples of applications of molecular mechanisms in breeding Based on the understanding of the molecular mechanisms of corn grain quality traits (Li et al., 2017), scientists have applied this knowledge in breeding practice and developed multiple new corn varieties with excellent quality. For example, using molecular marker-assisted selection (MAS) technology to select for specific key genes for quality traits can significantly improve the efficiency and accuracy of breeding. In addition, through gene editing technologies such as CRISPR/Cas9, scientists can precisely change key genes that affect quality traits, such as increasing the protein content of grains by editing the Opaque-2 (O2) gene. These breeding technologies based on molecular mechanisms not only speed up the breeding process of excellent varieties, but also provide a powerful tool for the continuous improvement of corn quality (Wang et al., 2023). Research on the molecular mechanisms of corn grain quality traits provides an important scientific basis for our in-depth understanding of the genetic basis of quality formation. It also provides effective strategies and methods for genetic improvement and variety optimization of corn (Figure 2). With the continuous development of molecular biology technology, the field of corn quality improvement will show greater potential and prospects in the future. 5 Future Research Directions and Prospects With the rapid development of molecular biology and genetics technology, the research on corn quality traits is in a period of unprecedented development opportunities. Future research directions will not only continue to deepen the understanding of the molecular mechanisms of corn quality traits, but also explore new technologies and methods in order to achieve greater progress in improving corn quality. 5.1 Further improvements in GWAS technology and methods Although genome-wide association analysis (GWAS) has become an important tool for revealing the genetic basis of complex traits in crops such as maize, its accuracy and efficiency still need to be improved. Future research needs to focus on further improving GWAS technology and methods, such as improving the analytical capabilities
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