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).
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