As one of the most important food crops in the world, corn plays an irreplaceable role in ensuring global food security. As the world's population continues to grow and food demand continues to rise, improving the yield and nutritional value of corn has become a top priority in research and breeding. This is not only about the quantity of food, but also about the quality of food, that is, how to significantly improve the nutritional content of corn through scientific methods to better meet the needs of human health diet. In this context, traditional breeding techniques can no longer fully meet the needs of modern agriculture, and new scientific and technological means are urgently needed to guide crop quality improvement.

In recent years, genome-wide association analysis (GWAS), as an emerging genetic research method, has been widely used in crop genetics and breeding research due to its unique advantages (Reddy et al., 2023). Compared with traditional genetic analysis methods, GWAS can identify genetic markers associated with specific traits on a genome-wide scale, providing an efficient tool for revealing the genetic basis of crop traits. This method is particularly suitable for analyzing complex traits that are jointly controlled by multiple small-effect genes, such as corn yield, disease resistance, and nutritional components, etc., thus greatly promoting the research progress of crop quality improvement (Karikari et al., 2023).

However, although GWAS has shown great potential in crop genetic research, how to accurately interpret GWAS results and apply this genetic information to actual corn breeding remains a challenge. In addition, corn quality improvement not only needs to consider adding specific nutrients, but also needs to take into account the crop's growth cycle, yield, and adaptability to environmental conditions, which increases the complexity of quality improvement.

This study aims to comprehensively use GWAS methods to deeply analyze the key genetic factors in corn quality improvement, especially those important genes and genetic loci that affect the nutritional composition of corn. Through this research, we expect to provide scientific basis and genetic resources for high-quality breeding of corn, and ultimately achieve the goal of improving the nutritional value and yield of corn (Sahito et al., 2024). At the same time, this study also aims to explore strategies for effectively applying GWAS results to breeding practice, and how to precisely regulate target traits in corn through the latest biotechnological means, such as gene editing. We believe that this research will not only advance research on corn genetics, but will also contribute to global food security.

1 Overview of GWAS Methods

Genome-wide association analysis (GWAS) is a method widely used in genetic research in recent years, especially in the field of crop genetic improvement, showing great potential. GWAS helps scientists reveal the genetic genes that influence complex traits by analyzing the association between genetic variations and traits.

1.1 Basic principles and methods of GWAS

The core of GWAS is to identify the association between specific traits and specific regions on the genome (Uffelmann et al., 2021). By scanning the genomes of large numbers of individuals, GWAS can identify single nucleotide polymorphisms (SNPs) or other genetic markers that are significantly associated with differences in traits. This process involves collecting genetic information and trait data from a large number of samples and then using statistical methods to analyze correlations between these data to identify genetic loci that may control the trait. This method does not rely on prior knowledge of candidate genes, giving it a unique advantage in dissecting the genetic basis of complex traits.

1.2 Examples of application of GWAS in crop genetic improvement

GWAS has been successfully used in research on genetic improvement of a variety of crops, such as disease resistance research in wheat, yield and quality improvement in rice, and stress resistance traits in corn (Liu and Yan, 2019) . These studies not only discovered a series of genetic loci associated with important agronomic traits, but also revealed the genetic mechanisms by which crops adapt to environmental changes. For example, in the study of rice yield improvement, scientists successfully identified dozens of key genetic loci related to yield through GWAS methods. These findings provide important genetic resources for molecular breeding of rice.

1.3 Specific challenges and solutions for GWAS in corn quality improvement

Although GWAS has shown great potential in crop genetic improvement, it also faces some specific challenges in the process of improving corn quality (Lin, 2022, Chinese Journal of Biotechnology, 41(12): 1-3). First of all, the genetic background of corn is complex, and quality-related traits are often controlled by multiple genes and are greatly affected by environmental factors, which increases the difficulty of GWAS analysis. Secondly, the collection of high-quality genetic markers and large-scale phenotypic data requires extremely high resources, which is a big challenge for some research teams.

To overcome these challenges, scientists have adopted a variety of strategies. On the one hand, the establishment of a large maize genetic resource library and phenotypic data set provides rich data support for GWAS research. On the other hand, the use of the latest genome sequencing technology, such as next-generation sequencing (NGS), improves the density and quality of genetic markers, making GWAS analysis more accurate. In addition, a series of advanced statistical methods have been developed to process complex genetic data, improving the accuracy and efficiency of analysis.

Through these efforts, GWAS methods have achieved remarkable results in corn quality improvement (Hao et al., 2018) . Not only did they successfully identify multiple key genetic loci related to corn quality, they also revealed the complex genetic network that affects many traits such as corn nutritional composition, disease resistance, and ability to adapt to the environment. For example, through GWAS analysis, scientists have discovered several genetic loci directly related to corn starch and protein content. These findings not only enrich our understanding of the genetic basis of corn quality, but also provide important genetic markers for precision breeding.

2 Genetic Basis of Corn Quality-related Traits

As an important food and feed crop in the world, corn occupies an indispensable position in the daily diet of humans and animals. As people's requirements for food nutrition and health increase, improving the nutritional quality of corn has become the focus of current breeding research. The improvement of corn quality is not only related to the content of its main components such as starch, protein, and oil, but also involves how to effectively control these traits through genetic improvement. Therefore, an in-depth understanding of the genetic basis that affects corn quality traits is of great scientific significance and application value for achieving precision breeding.

2.1 Define key traits that affect corn quality

Among the many traits that affect corn quality, starch content, protein content, and oil content are regarded as the most critical indicators. As the main carbohydrate component of corn, starch content directly affects the energy supply capacity and processing characteristics of corn. Protein content defines the quality of corn from a nutritional perspective, and high-protein corn is more suitable as feed or food with high nutritional value. The increase in oil content can not only increase the energy density of corn, but also improve the taste and flavor of food, which is particularly important for the production of high-quality corn oil (Wang et al., 2007) .

2.2 Describe the major genetic factors known to influence these traits

With the rapid development of molecular genetics and genomics, multiple key genetic factors affecting corn quality traits have been identified. The genetic regulation of starch content is complex and involves the expression regulation of multiple enzyme genes such as starch synthase and amylopectin synthase (Yue et al., 2005) . For example, variations in the GBSS1 gene can affect amylose synthesis, thereby affecting starch content and properties. The regulation of protein content involves the expression of storage protein genes such as Zea mays Prolamin Box Binding Factor (Opaque2). The expression level of this gene directly affects the accumulation of protein in the grain. The genetic regulation of oil content is closely related to genes related to fatty acid synthesis and fatty acid metabolism. For example, variations in the fatty acid synthase gene ACCase and the key oil synthesis enzyme gene DGAT can significantly affect the oil content in corn kernels (Figure 1).

Figure 1 Manhattan pole and QQ pole of genome-wide association study (Chaudhary et al., 2016) Note: A: Protein content; B: Starch content; C: Oil content

Further research shows that the genetic factors that affect these traits include not only the functional variation of a single gene, but also the interactions between genes, epigenetic regulation, and the interaction between genes and 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)

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).

Figure 2 Gene ontology (GO) enriched terms associated with diferentially genes expressed (DEGs) in starch content (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.

4.2 Challenges of data integration and cross-population verification

The success of GWAS relies heavily on large-scale and high-quality genetic and phenotypic data. However, there may be differences in the sample populations, phenotypic evaluation methods, and genotype platforms used in different studies, and these differences increase the difficulty of data integration. In addition, the genetic markers discovered by GWAS may have different effects in maize populations with different genetic backgrounds, and cross-population verification in multiple populations is required to ensure that the discovered markers have universal applicability. This process requires a lot of resources and energy and is a major challenge in applying GWAS results to actual breeding.

4.3 How technological progress can overcome existing challenges

In recent years, the rapid development of biotechnology has provided new solutions to overcome challenges in GWAS applications. High-throughput sequencing technology makes large-scale genome sequencing more cost-effective and provides the possibility to collect high-quality genetic data. Advances in these technologies not only accelerate the process of GWAS research, but also provide a more unified genomic reference for data integration. In addition, the emergence of CRISPR gene editing technology provides a powerful tool for functional verification and direct utilization of genetic resources discovered by GWAS. Through CRISPR technology, specific sites in the corn genome can be precisely edited, its impact on traits can be directly verified, and this knowledge can be applied in breeding to achieve precise improvement of specific traits.

In short, the application of GWAS in corn quality improvement has shown great potential and value (Guan et al., 2016). Although there are challenges in data integration, cross-population validation, and application of research results to practical breeding, these challenges are gradually being overcome as biotechnology continues to advance. In the future, GWAS combined with advanced technologies such as high-throughput sequencing and CRISPR gene editing will further promote the precision and efficiency of corn breeding. This not only means that new corn varieties with high yield, high resistance, high nutrition and other characteristics can be bred more quickly, but will also greatly promote the sustainable development of the corn industry and global food security.

5 Future Directions and Outlook

With the rapid development of genomics, bioinformatics and molecular biology technologies, the application of genome-wide association analysis (GWAS) in the field of corn quality improvement is ushering in new development opportunities. In the future, the application of GWAS will be more extensive and in-depth, especially the integration with other genetic methods, which will bring innovation to the genetic improvement and breeding strategies of corn.

5.1 Integrated application of GWAS and other genetic methods

The integration of GWAS with other genetic methods, such as linkage analysis and functional genomics, can more fully reveal the relationship between genetic variation and quality traits in maize. Linkage analysis can help identify genetic markers associated with traits in a specific genetic background, while functional genomics can provide insights into the biological mechanisms behind these markers. This multi-angle, multi-level research method will enable us to more accurately predict and manipulate the genetic traits of corn, and improve the efficiency and accuracy of breeding.

5.2 The potential of multi-phenotypic GWAS in improving complex traits of maize

Multi-phenotypic GWAS, that is, GWAS that analyzes multiple related traits simultaneously, provides a new perspective for analyzing complex traits in maize (Guo et al., 2019). This method can reveal genetic correlations and interactions between different traits and help identify shared genetic factors that influence multiple traits. In corn quality improvement, this means that multiple traits can be improved simultaneously, such as increasing yield while increasing disease resistance and nutritional value, thereby cultivating new more comprehensive and excellent corn varieties.

5.3 Consideration of the interaction between environmental factors and genetic factors in improving corn quality

The growth and development of corn are affected by both genetic and environmental factors. Future GWAS studies need to consider more the interaction between the two, that is, how to express the optimal genetic potential under different environmental conditions. By studying how environmental factors affect the expression of trait-related genes, we can provide scientific basis for the environmental adaptability and stability improvement of corn, ensuring optimal quality and yield under different regions and climate conditions.

5.4 The contribution of GWAS in revealing key genetic factors related to corn quality improvement

GWAS has made remarkable achievements in research on corn quality improvement and will continue to play an important role in the future. As GWAS research deepens, more key genetic factors affecting corn quality will be discovered. These research results can not only enrich our understanding of the genetic diversity of maize, but also provide powerful genetic resources for precision breeding and genetic improvement.

5.5 Prospects for future corn genetic improvement and breeding strategies

Looking to the future, corn genetic improvement and breeding strategies will rely more heavily on GWAS and its integration with other technologies. By precisely manipulating the corn genome, combined with the goals of environmental adaptability and multi-trait optimization, breeders can develop new corn varieties that better meet market and consumer needs. In addition, with the mature application of gene editing technologies such as CRISPR-Cas systems, and the application of artificial intelligence and machine learning in genetic data analysis, future corn breeding will be more efficient and precise.

In the future, corn breeding will not only focus on improving yield and quality, but also pay attention to the nutritional value, disease resistance, stress tolerance and environmental adaptability of the crop (Hageman et al., 1967). For example, relevant genetic loci discovered through GWAS can help scientists design corn varieties that are more drought- or salt-tolerant, or varieties that can maintain high yields with lower fertilizer use, thus promoting the sustainable development of agriculture. In the process, interdisciplinary collaboration will become increasingly important. Geneticists, molecular biologists, breeders, ecologists, and information scientists will need to work closely together to solve scientific problems and technical challenges encountered in the breeding process. Through this multidisciplinary cooperation, we can more comprehensively understand the growth and development laws of corn, utilize genetic resources more effectively, and ultimately achieve efficient and sustainable corn breeding goals.

GWAS and its integrated application with other genetic methods provide new directions and powerful tools for corn quality improvement and breeding strategies. With the continuous advancement of technology and the deepening of scientific research, future corn breeding will be able to better meet the needs of global food security and sustainable agricultural development.

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