Maize Genomics and Genetics 2024, Vol.15 http://cropscipublisher.com/index.php/mgg © 2024 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
Maize Genomics and Genetics 2024, Vol.15 http://cropscipublisher.com/index.php/mgg © 2024 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. CropSci Publisher is an international Open Access publishing specializing in maize genome, trait-controlling, maize gene expression and regulation at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada Publisher Cropsci Publisher Editedby Editorial Team of Maize Genomics and Genetics Email: edit@mgg.cropscipublisher.com Website: http://cropscipublisher.com/index.php/mgg Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Maize Genomics and Genetics (ISSN 1925-1971) is an open access, peer reviewed journal published online by CropSci Publisher. The journal is committed to publishing basic theories, novel techniques, and new advances within all aspects of maize research, especially focusing on genetics and genomics. Papers regarding classical genetics analysis, structural and functional analysis of maize genome, trait-controlling, maize gene expression and regulation, transgenic maize, as well as maize varietal improvement, are especially welcomed. All the articles published in Maize Genomics and Genetics are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CropSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
Maize Genomics and Genetics (online), 2024, Vol. 15, No.1 ISSN 1925-1971 http://cropscipublisher.com/index.php/mgg © 2024 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Genome-Wide Association Study of Maize Kernel Quality Related Traits and Their Molecular Mechanisms Jin Zhou, Wenying Hong Maize Genomics and Genetics, 2024, Vol. 15, No. 1, 1-8 Unraveling Key Genetic Factors in Corn Quality Improvement through GWAS Tianxia Guo Maize Genomics and Genetics, 2024, Vol. 15, No. 1, 9-17 The Genetic Basis of Nutritional Quality Traits in Maize: Insights from GWAS Liang Li, Wenzhong Huang Maize Genomics and Genetics, 2024, Vol. 15, No. 1, 18-26 The Role of Isoenzymatic Variation in Delineating Phylogenetic Relationships within Zea WeiWang Maize Genomics and Genetics, 2024, Vol. 15, No. 1, 27-35 Teosinte and Maize: Comparative Genomics and Agricultural Impact JiongFu Maize Genomics and Genetics, 2024, Vol. 15, No. 1, 36-48
Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 1 Research Article Open Access Genome-Wide Association Study of Maize Kernel Quality Related Traits and Their Molecular Mechanisms Jin Zhou , Wenying Hong Hainan Provincial Key Laboratory of Crop Molecular Breeding, Sanya, 572000, Hainan, China Corresponding author: 3048511772@qq.com Maize Genomics and Genetics, 2024, Vol.15, No.1 doi: 10.5376/mgg.2024.15.0001 Received: 06 Dec., 2023 Accepted: 09 Jan., 2024 Published: 20 Jan., 2024 Copyright © 2024 Zhou and Hong, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhou J., and Hong W.Y., 2024, Genome-wide association study of maize kernel quality related traits and their molecular mechanisms, Maize Genomics and Genetics, 15(1): 1-8 (doi: 10.5376/mgg.2024.15.0001) Abstract In recent years, significant progress has been made in revealing the genetic basis and molecular mechanisms underlying maize kernel quality traits, thanks to the development and application of Genome-Wide Association Studies (GWAS). Kernel quality traits in maize, such as starch, protein, and oil content, not only directly affect its nutritional value and processing quality but are also crucial for enhancing food security. This study summarizes the application of GWAS in the study of maize kernel quality traits, including the discovery of key genes and loci, how these genes regulate specific quality traits, and their potential applications in maize breeding. Furthermore, the study discusses the challenges and limitations of GWAS research, as well as future directions, particularly in the application of high-throughput sequencing technologies, precise gene editing, and integration of multi-omics data analysis, aiming to further improve maize quality. By deeply understanding the genetic and molecular mechanisms of maize kernel quality traits, this study highlights the importance and prospects of molecular breeding in the improvement of crop quality. Keywords Genome-wide association studies (GWAS); Maize; Kernel quality; Genetic basis; Molecular mechanisms; Breeding improvement As a food crop with the largest planting area and output worldwide, corn is not only one of the main food sources for humans, but also a key raw material for animal feed, bioenergy and various industrial raw materials. Corn occupies an irreplaceable position in global food security and agricultural economy, and its production and quality are directly related to the stability of the food supply chain and human health. The quality of corn kernels involves many aspects such as the starch, protein, and oil content of the kernels. These quality traits not only determine the nutritional value of corn, but also affect its performance in processing and industrial utilization (Yano et al., 2016). For example, corn with high starch content is suitable for deep processing as an energy plant, while corn with high protein and high oil content is more favored by the food and feed industry. Therefore, improving the quality of corn kernels can not only meet people's demand for food with high nutritional value, but is also of great significance for increasing the economic value of corn. In recent years, genome-wide association analysis (GWAS), as a powerful genetic research tool (Uffelmann et al., 2021), has shown great potential in the field of crop genetic improvement. By analyzing the association between genetic variation and trait phenotype, GWAS can accurately locate key genes or genetic markers that affect specific traits across the entire genome. This method not only greatly accelerates the pace of crop genetic improvement, but also provides a new perspective for us to understand the genetic mechanisms of complex traits. Although GWAS has achieved remarkable results in many fields, its application in the study of corn grain quality traits still faces many challenges, such as the complexity of traits, the influence of environmental factors, and the processing of large-scale genetic variation data. Therefore, this study aims to review the latest progress of GWAS in analyzing the molecular mechanisms of corn grain quality traits, and explore the application and potential of GWAS methods in identifying molecular markers of related traits, revealing the molecular pathways of trait formation, and guiding molecular breeding of maize (Schaid et al., 2018).
Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 2 By summarizing and analyzing the research results in recent years, we hope to better understand the genetic basis of corn grain quality and provide scientific basis for future genetic improvement and variety selection. At the same time, this study will also discuss the limitations of GWAS in current technologies and methods, as well as possible breakthrough directions in future research, aiming to provide reference and inspiration for scholars in the fields of corn quality improvement and genetic research. 1 Overview of Corn Kernel Quality Traits As one of the important food crops in the world, corn's grain quality traits are directly related to its application value in food, feed and industrial processing (Dai et al., 2007). The quality traits of corn kernels mainly include starch content, protein content, oil content, etc. The level of these traits determines the nutritional value and processing characteristics of corn. As people's demands for health and nutrition continue to increase, attention to corn quality traits is also increasing. 1.1 Classification of quality traits Starch, protein, and oil are the three main components that make up corn kernels, and each has a significant impact on the energy value and processing potential of corn. Starch is the main component of corn kernels, and its content directly determines the energy value of corn. The level of starch content plays a crucial role in the processing of corn such as alcohol fermentation and saccharification. Generally speaking, the higher the starch content of corn, the greater its value in processing and utilization (Liu, 2004, Cereal Crops, 24(5): 258-260.). Protein is another key factor affecting the nutritional value of corn. It not only determines the quality of corn as food and feed, but also affects the quality of corn processed products. Corn with higher protein content is more suitable for the production of high-protein feed and food to meet the market demand for products with high nutritional value. The oil content mainly affects the energy value of corn and the extraction efficiency and quality of corn oil. The oil in corn kernels is mainly concentrated in the endosperm. The oil content is not only related to the energy value, but also directly affects the preference of different corn varieties in the edible oil processing industry. Typically, corn varieties with higher oil content are more popular in the edible oil processing industry because they can provide higher oil extraction rates and better oil quality. 1.2 Impact of quality traits on corn processing and consumption The quality traits of corn kernels play a vital role in its processing and consumption. Different quality traits make corn have specific suitability in multiple fields (Zheng et al., 2021): corn with high starch content is most suitable for the production of alcohol and starch; while corn with high protein content is more suitable for the production of high protein Food and feed; at the same time, corn with high oil content is mainly used to extract corn oil. In the processing industry, the quality traits of corn are key factors that determine product yield and quality. For example, starch content not only directly affects alcohol production and starch extraction rate, but oil content also affects the output and quality of corn oil. Therefore, according to the different needs of processed products, the processing industry has clear requirements for the quality traits of corn. In the consumer market, the quality traits of corn are also an important criterion for consumer selection (Zeng et al., 2022). Because corn with high protein and oil content has higher nutritional value, this type of corn is favored by health-conscious consumers. In addition, corn varieties with specific quality traits. For example, sweet corn and waxy corn are very popular in the consumer market because of their unique taste and flavor. Sweet corn contains high sugar content and is suitable for eating directly or processing into canned products, frozen products, etc. Because of its unique sticky texture, waxy corn is often used to make traditional delicacies such as glutinous rice cakes and rice dumplings. These special quality corn varieties meet consumer demand for food variety and special taste. These situations collectively reflect that the quality traits of corn kernels not only affect its application in the processing field, but also profoundly affect consumer choices and consumption behaviors.
Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 3 2 Introduction to Genome-wide Association Analysis (GWAS) Genome-wide association analysis (GWAS) is a method widely used in genetic research to identify genetic markers that influence a specific trait by analyzing the association between genetic variation in the genome and the trait. The emergence of GWAS marks a new era in genetic research, especially in the field of plant genetics, which provides a powerful tool for analyzing the genetic basis of complex traits. 2.1 Principles of GWAS and its application in plant genetics The basic principle of GWAS is to use genomic data of a large number of individuals (Uffelmann et al., 2021) to find those single nucleotide polymorphisms (SNPs) that are significantly associated with phenotypic variation of specific traits through statistical analysis. These SNPs may be located in the coding region of the gene or in the regulatory region, affecting gene expression. In plant genetics research, GWAS is widely used to analyze important agronomic traits of crops, such as yield, disease resistance, quality, etc. Compared with traditional genetic analysis methods, GWAS can directly identify genetic variations related to traits across the entire genome without knowing the gene location in advance, greatly improving the efficiency and accuracy of genetic research (Cortes et al., 2021). Through GWAS, researchers can identify key genetic factors affecting specific traits in natural populations with rich genetic diversity (Figure 1), which is of great significance for the genetic improvement of crops and the cultivation of new varieties. For example, through GWAS analysis, scientists have successfully identified multiple key genes that affect corn grain quality, wheat disease resistance, and rice yield. These findings not only enrich our understanding of the genetic mechanisms of these traits, but also provide information for crop breeding. new target. Figure 1 Different genome-wide association study methods ask different questions 2.2 Comparison of GWAS and traditional genetic marker association studies Although GWAS has shown its advantages over traditional genetic marker association studies in many aspects (Brachi et al., 2011), there are also certain complementarities between the two in practical applications. Traditional genetic marker association studies, such as linkage analysis, usually look for markers related to traits by analyzing genetic data of specific groups (such as families or inbred lines) in a limited genetic background. This method relies on genetic linkage and is therefore somewhat limited in positioning accuracy, especially in the study of complex traits. In contrast, because GWAS is conducted in a wide range of natural populations, it can take advantage of the large number of genetic recombination events accumulated in natural populations to accurately locate genetic variations associated with traits across the entire genome. In addition, GWAS can also reveal the interaction of multiple genes in the formation of traits, making it possible to understand the genetic complexity of traits. However, GWAS also has its limitations, such as the high requirements on population structure and degree of association, and the so-called "missing heritability" problem, that is, GWAS sometimes cannot explain all genetic variation. 3 Application of GWAS in the Study of Corn Grain Quality Traits Genome-wide association analysis (GWAS), as a powerful genetic research tool, has been widely used in the study of corn grain quality traits. It provides an efficient method to reveal the genetic basis that affects corn quality traits by analyzing the association between genetic variation and trait phenotypes.
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
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
Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 6 of GWAS by developing more efficient statistical analysis models, improving the accuracy and reducing costs of genotype sequencing technology, and optimizing sample selection and design. In addition, using artificial intelligence and machine learning technology to process and analyze large-scale genetic data is also an important direction for future GWAS research. Figure 2 Pleiotropy for kernel traits in maize-teosinte NILs populations Note: NIL: Infiltration system; PC: Principal component; QTL: Quantitative trait loci; Wt50k: Weighing 50 pills 5.2 The application prospects of multi-omics data integration analysis in revealing the molecular mechanisms of complex traits With the development of multi-omics technologies such as transcriptomics, proteomics, and metabolomics, integrated analysis of these different levels of biological information has become an important means to reveal the genetic mechanisms of complex traits. In the study of corn quality traits, by integrating multi-omics data such as GWAS, transcriptome, and metabolome, we can more comprehensively understand the formation mechanism of quality traits, which is especially valuable in analyzing the complex regulatory network between genes and traits. . In the future, establishing a more effective multi-omics data integration and analysis platform will help discover new genetic resources and regulatory pathways, and provide more accurate molecular markers for improving corn quality. 5.3 The potential of precision breeding in improving corn quality Precision breeding technologies, especially gene editing technologies such as CRISPR/Cas9, provide new strategies for improving corn quality traits (Hao et al., 2018). Compared with traditional breeding methods, gene editing technology can accurately modify target genes without introducing exogenous DNA, thereby quickly obtaining the desired quality traits. In the future, through more in-depth research on gene functions and optimization of editing technology, the efficiency and safety of precision breeding will be further improved. At the same time, combined with the key genes and regulatory networks discovered by GWAS and multi-omics data analysis, precision breeding is expected to play a greater role in improving corn quality and provide strong support to meet human needs for high-quality corn. 5.4 The importance of molecular mechanisms for future corn improvement Although GWAS has made a lot of progress in the study of corn quality traits, in order to achieve fundamental improvements in corn quality, in-depth research on the molecular mechanisms of the formation of these traits is still needed. Future research needs to focus on revealing the functions of key genes and their mechanisms in the formation of corn quality, which is of vital significance for accurately regulating corn quality traits and cultivating new varieties that are more suitable for market demand. In addition, with the development of precision breeding technologies such as gene editing, an in-depth understanding of molecular mechanisms will allow us to more precisely manipulate these key genes to achieve targeted improvements in corn quality.
Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 7 The application of GWAS in the study of corn grain quality traits has made important progress, providing valuable genetic information and molecular tools for corn quality improvement. However, to achieve sustained and fundamental improvements in corn quality, we still need to continue to study the molecular mechanisms of quality traits in depth, and at the same time, combine modern biotechnology to continuously explore and develop new breeding strategies. In the future, with the continuous progress of science and technology, I believe we can overcome the current challenges and make greater contributions to global food security and sustainable development. Acknowledgments We would like to express our gratitude to the two anonymous peer reviewers for their critical assessment and constructive suggestions on our manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Brachi B., Morris G.P., and Borevitz J.O., 2011, Genome-wide association studies in plants: the missing heritability is in the field, Genome Biology, 12: 1-8. https://doi.org/10.1186/gb-2011-12-10-232 PMid:22035733 PMCid:PMC3333769 Cortes L.T., Zhang Z., and Yu J., 2021, Status and prospects of genome-wide association studies in plants, The Plant Genome, 14(1): e20077. https://doi.org/10.1002/tpg2.20077 PMid:33442955 Dai H.X., Xiong Y.Z., and Niu J.H., 2007, Advances of inheritance of quality characters in sweet corn, Changjiang Shucai (Journal of Changjiang Vegetables), (10): 28-31. Guo J.J., Liu W.S., Zheng Y.X., Liu H., Zhao Y.F., Zhu L.Y., Huang Y.Q., Jia X.Y., and Chen J.T., 2019, Genome-wide association analysis of maize (Zeamays) grain quality related traits based on four test cross populations, Nongye Shengwu Jishu Xuebao (Journal of Agricultural Biotechnology), 27(5): 809-824. Guo X., Ge Z., Wang M., Zhao M., Pei Y., and Song X., 2023, Genome-wide association study of quality traits and starch pasting properties of maize kernels, BMC Genomics, 24(1): 59. https://doi.org/10.1186/s12864-022-09031-4 PMid:36732681 PMCid:PMC9893588 Hao H.Q., Liu L.L., Yao Y., Feng X., Li Z.G., Chao Q., Xia R., Liu H.T., Wang B.C., Qin F., Xie Q., and Jing H.C., 2018, Application and prospect of molecular module-based crop design technology in maize breeding, Zhongguo Kexueyuan Yuankan (Bulletin of Chinese Academy of Sciences), 33(9): 923-931. Li T.C., Yang H.Y., Liu G.H., Zhang W., Dong Q., Lei Y.L., Qian Y.L., Zhou Y.B., and Chen H.J., 2017, Advances on molecular mechanism of anthocyanins biosynthesis for maize seed, Molecular Plant Breeding, 15(7): 2623-2627. Liu Z., Garcia A., McMullen M.D., and Flint-Garcia S.A., 2016, Genetic analysis of kernel traits in maize-teosinte introgression populations, G3: Genes, Genomes, Genetics, 6(8): 2523-2530. https://doi.org/10.1534/g3.116.030155 PMid:27317774 PMCid:PMC4978905 Schaid D.J., Chen W., and Larson N.B., 2018, From genome-wide associations to candidate causal variants by statistical fine-mapping, Nature Reviews Genetics, 19(8): 491-504. https://doi.org/10.1038/s41576-018-0016-z PMid:29844615 PMCid:PMC6050137 Uffelmann E., Huang Q.Q., Munung N.S., De Vries J., Okada Y., Martin A.R., Lappalainen T., and Posthuma D., 2021, Genome-wide association studies, Nature Reviews Methods Primers, 1(1): 59. https://doi.org/10.1038/s43586-021-00056-9 Uffelmann E., Huang Q.Q., Munung N.S., De Vries J., Okada Y., Martin A.R., Martin H.C., Lappalainen T., and Posthuma D., 2021, Genome-wide association studies, Nature Reviews Methods Primers, 1(1): 59. https://doi.org/10.1038/s43586-021-00056-9 Wang C., Li H., Long Y., Dong Z., Wang J., Liu C., Wan X., and Wan X., 2023, A systemic investigation of genetic architecture and gene resources controlling kernel size-related traits in maize, International Journal of Molecular Sciences, 24(2): 1025. https://doi.org/10.3390/ijms24021025 PMid:36674545 PMCid:PMC9865405 Wen W., Li D., Li X., Gao Y., Li W., Li H., Chen W., Luo J., and Yan J., 2014, Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights, Nature Communications, 5(1): 3438. https://doi.org/10.1038/ncomms4438 PMid:24633423 PMCid:PMC3959190
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Maize Genomics and Genetics 2024, Vol.15, No.1, 9-17 http://cropscipublisher.com/index.php/mgg 9 Research Article Open Access Unraveling Key Genetic Factors in Corn Quality Improvement through GWAS Tianxia Guo Institute of Life Sciences, Jiyang College, Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding email: 3048511772@qq.com Maize Genomics and Genetics, 2024, Vol.15, No.1 doi: 10.5376/mgg.2024.15.0002 Received: 10 Dec., 2023 Accepted: 12 Jan., 2024 Published: 31 Jan., 2024 Copyright © 2024 Guo, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Guo T.X., 2024, Unraveling key genetic factors in corn quality improvement through GWAS, Maize Genomics and Genetics, 15(1): 9-17 (doi: 10.5376/mgg.2024.15.0002) Abstract Genome-Wide Association Studies (GWAS) are a powerful genetic tool that has been widely applied in the field of crop quality improvement in recent years. Particularly in corn, as one of the world's important food and feed crops, improving its quality is crucial to meet the growing demand for food. This study reviews the application of GWAS in analyzing the genetic basis of corn quality-related traits, focusing on the genetic regulatory mechanisms of key traits such as starch content, protein content, and oil content. We summarize recent research progress, including key genetic loci discovered through GWAS and their potential impact on corn quality. Moreover, we discuss the challenges and opportunities of applying GWAS findings in corn breeding practices, and how to utilize the latest biotechnologies, such as CRISPR gene editing, for precise trait improvement in corn. Finally, the study proposes future research directions, emphasizing the importance of integrating various genetic and genomic tools to comprehensively understand the genetic mechanisms behind corn quality formation. Keywords Quality improvement; Genetic factors; CRISPR gene editing; Genetic loci; Biotechnology 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
Maize Genomics and Genetics 2024, Vol.15, No.1, 9-17 http://cropscipublisher.com/index.php/mgg 10 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
Maize Genomics and Genetics 2024, Vol.15, No.1, 9-17 http://cropscipublisher.com/index.php/mgg 11 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) (Chaudhary et al., 2016). 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
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
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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