Maize Genomics and Genetics 2024, Vol.15, No.1, 9-17 http://cropscipublisher.com/index.php/mgg 13 3.2 Case study 2: using GWAS to reveal the genetic network regulating protein content in corn Another GWAS study focused on the genetic regulation of protein content in corn (Sahito et al., 2024). By analyzing the genomic data and protein content phenotypic data of different corn varieties, the study successfully identified multiple genetic loci closely related to protein content. These sites are distributed in different regions of the maize genome and involve multiple key genes that regulate protein synthesis and metabolism. In particular, genes near some loci are involved in nitrogen uptake and utilization pathways, indicating a potential genetic link between protein content and efficient utilization of nitrogen nutrients in maize. This research not only provides a new perspective for understanding the genetic regulation of protein content, but also provides target genes for improving corn protein content. 3.3 Case study 3: application of GWAS in analyzing the genetic regulation of corn oil content In a GWAS study on corn oil content, scientists successfully identified multiple genetic loci related to oil content by analyzing corn populations in multiple environments. These loci cover a series of genes with different functions, including genes related to fatty acid synthesis, transport, and lipid accumulation. It is worth noting that genes near certain genetic loci play a role in key nodes of lipid metabolism, such as fatty acid synthase (FAS) and lipid synthesis-related enzymes (DGAT). These findings not only improve our genetic regulation of lipid content The understanding of the mechanism also provides target genes for improving corn oil content through genetic improvement. Through the discovery of these genetic loci, researchers can further explore how specific genes affect the synthesis and accumulation process of oil. For example, genes near certain genetic loci may subtly regulate oil content by regulating fatty acid biosynthetic pathways, or by affecting the distribution and accumulation of oil in corn kernels. The analysis of these details provides in-depth theoretical basis and practical guidance for corn quality improvement. These case studies demonstrate how GWAS plays a role in improving corn quality (Ruanjaichon et al., 2021) . Through genome-wide association analysis, researchers can not only identify key genetic loci related to specific quality traits, but also reveal The complex genetic regulatory networks underlying these traits. These findings provide valuable genetic resources for improving corn quality, allowing breeding efforts to improve specific traits more accurately. More importantly, the knowledge and resources obtained through GWAS can not only be applied to traditional breeding programs, but also provide target genes for the use of advanced molecular breeding technologies, such as gene editing (CRISPR/Cas9, etc.). This means that scientists can edit specific regions in the corn genome more precisely to directly improve key genes that affect starch content, protein content and oil content, and then breed new varieties with excellent quality traits (Figure 2) (Ruanjaichon et al., 2021). 4 Applications and Challenges of GWAS Results Genome-wide association analysis (GWAS) has made remarkable achievements in revealing the genetic basis of quality traits in crops such as corn. However, applying these research results to actual breeding work to improve crop quality and yield faces many challenges. This section will explore how to apply key genetic factors discovered by GWAS to the challenges of corn breeding, data integration and cross-population validation, and how technological advances can help overcome these challenges. 4.1 How to apply key genetic factors discovered by GWAS to corn breeding GWAS provides powerful genetic information for precision breeding by identifying genetic markers significantly related to corn quality traits (de Souza Camacho et al., 2019). Applying this information to breeding first requires functional verification of the discovered genetic markers to ensure that these markers are actually involved in regulating the target traits. Next, these beneficial genes can be tracked and selected during the breeding process through molecular marker-assisted selection (MAS) technology, thereby accelerating the breeding process and improving the accuracy of selection. In addition, based on GWAS results, breeders can design hybrid strategies to create hybrid combinations with excellent performance by purposefully combining parents with desirable traits.
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