Genomics and Applied Biology 2024, Vol.15, No.1, 12-21 http://bioscipublisher.com/index.php/gab 13 This study also discusses the current challenges of GWAS in improving corn disease resistance, such as functional verification of candidate genes, the impact of environment and genotype interactions, and the challenges of big data analysis. At the same time, future research directions are looked forward to, including using multi-omics data to improve the accuracy of GWAS, and accelerating the development of disease-resistant corn varieties by integrating genetic resources and new breeding technologies. This study aims to review the application of GWAS in corn disease resistance research and discuss the entire process from theoretical basis to practical application. The article first reviews the background of corn disease resistance research and the basic principles of genome-wide association studies, then details the successful cases of identifying corn disease resistance genes through GWAS, and discusses how these findings can be translated into practical breeding strategies. Finally, the challenges and future development directions of GWAS in improving corn disease resistance were discussed, aiming to provide scientific basis and new ideas for corn disease resistance breeding. Through this structural arrangement, this study hopes to provide a comprehensive perspective for corn disease resistance research and breeding practice. 1 Theoretical Basis of GWAS in Research on Corn Disease Resistance 1.1 Principles and methods of GWAS Genome-wide association studies (GWAS) is a powerful genetic research method that uses statistical methods to find associations between genetic variations and phenotypic traits. The core principle of this method is based on the theory of population genetics, that is, in natural populations, there may be a correlation between specific genetic variations (such as single nucleotide polymorphisms, SNPs) and variations in certain phenotypic traits. By analyzing genetic variation and phenotypic data from a large number of samples, GWAS can identify genes or gene regions related to target traits across the entire genome. The key technical steps of GWAS mainly include sample collection and genotype determination, accurate measurement of phenotypic data, statistical analysis, and verification of associated signals. First, researchers need to collect a sufficient number of samples and conduct genotype determination on the samples through high-throughput sequencing technology or gene chip technology to obtain a large amount of genetic marker information. Subsequently, precise measurement of phenotypic traits in each sample is critical to increase the accuracy of GWAS in discovering true association signals. After the data is prepared, the association between genetic markers and phenotypes is analyzed through statistical analysis methods (such as linear mixed models) to identify genetic variants associated with the target traits. Finally, the association signals discovered by GWAS are verified through further genetic and functional studies to ensure their true role in the target traits (Zhang, 2017). In plant disease resistance research, GWAS are widely used to identify genes or gene regions that control disease resistance traits. Since plant disease resistance traits are often complex traits controlled by multiple genes, traditional genetic analysis methods have limitations in the study of these traits. GWAS can systematically explore genetic variations related to disease resistance across the entire genome without knowing the gene function in advance, providing an effective method for revealing the genetic basis of plant disease resistance. GWAS can not only discover known disease resistance genes, but also reveal new and unexpected disease resistance-related genes or gene regions, which greatly promotes the understanding of plant disease resistance mechanisms and the development of disease-resistant breeding materials. 1.2 Genetic background of corn disease resistance genes Genome-wide association studies (GWAS) is a powerful genetic research tool that identifies effects by analyzing genome-wide associations between genetic markers (such as single nucleotide polymorphisms, SNPs) and specific traits. Genes or gene regions for these traits. This method is particularly suitable for studying the genetic basis of complex traits, such as disease resistance in corn, which is a typical quantitative trait that is jointly affected by multiple genes and environmental factors.
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