Genomics and Applied Biology 2024, Vol.15, No.1, 12-21 http://bioscipublisher.com/index.php/gab 15 The application of GWAS in the study of corn disease resistance requires precise data processing and analysis methods, combined with statistical models and correlation analysis, to identify genetic markers related to disease resistance traits. This process not only deepens our understanding of the genetic basis of disease resistance in maize, but also provides scientific basis and tools for the practice of disease resistance breeding in maize. 2 Experimental Design and Implementation of GWAS 2.1 Sample selection and genotype identification Genome-wide association studies (GWAS) has become an important tool for analyzing the genetic basis of complex traits such as maize. In the study of corn disease resistance, GWAS can reveal the association between specific genetic variations and disease resistance traits, providing scientific basis for disease management and resistance breeding. Key steps to achieve this include selection of representative samples, extraction of high-quality DNA, and accurate genotyping. When conducting GWAS, it is crucial to select a sample collection with high genetic diversity. In corn disease resistance research, this often means collecting corn varieties or hybrids from different geographical locations with different disease resistance phenotypes. The diversity of samples ensures that GWAS can cover a wide enough range of genetic variation, thereby increasing the possibility of discovering genetic markers associated with disease resistance. For example, Flint-Garcia et al. (2005) used an extensive collection of maize germplasm in their study, including cultivars from different geographical regions to ensure diversity of genetic background, which is important for revealing the factors associated with maize disease resistance. High-quality DNA extraction is another key factor in the success of GWAS. In corn, DNA is usually extracted from young leaf tissue using methods including the CTAB method or commercial DNA extraction kits. No matter which method is used, it is necessary to ensure that the extracted DNA is of high purity and integrity, and is suitable for subsequent high-throughput genotyping. For example, Murray and Thompson (1980) is widely used in the extraction of plant DNA because it can effectively remove polysaccharide and protein contamination. With the development of biotechnology, a variety of high-throughput genotype determination technologies have been applied to GWAS, including single nucleotide polymorphism (SNP) chips, whole-genome resequencing and sequence-specific amplification sequencing (Amplicon sequencing). SNP arrays are a cost-effective technology capable of detecting tens to millions of SNP sites throughout the genome. Whole-genome resequencing provides more comprehensive genetic variation information and is especially suitable for sample collections with diverse genetic backgrounds. In corn disease resistance research, GWAS often rely on these techniques to accurately identify genetic variants associated with disease resistance. Through careful sample selection, high-quality DNA extraction, and accurate genotyping, GWAS can effectively reveal the genetic basis of disease resistance traits in maize. The successful implementation of these steps lays the foundation for discovering new disease resistance genes, understanding disease resistance mechanisms, and cultivating more disease-resistant corn varieties. 2.2 Collection and processing of phenotypic data Genome-wide association studies (GWAS) provides an effective research method to reveal the genetic basis of disease resistance traits in maize. The theoretical basis of GWAS is based on the correlation studies between large-scale genetic variation and phenotypic traits. By comparing the genetic variation of individuals with different phenotypes across the entire genome, it aims to identify genes or gene regions associated with specific traits. The experimental design of GWAS usually includes sample selection, genotype determination, and collection of phenotypic data. First, it is critical to select a corn population with sufficient genetic diversity, which often includes multiple varieties or hybrids from different geographical locations with different genetic backgrounds. Genotype determination relies on high-throughput genome sequencing technology, such as single nucleotide polymorphism (SNP) chips or next-generation sequencing (NGS), to obtain genetic marker information covering the entire genome.
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