Genomics and Applied Biology 2024, Vol.15, No.1, 12-21 http://bioscipublisher.com/index.php/gab 16 In corn disease resistance research, accurate collection of phenotypic data is the key to achieving effective GWAS. The collection of phenotypic data typically involves the evaluation of corn plant responses to natural or artificially simulated disease environments. For example, researchers may inoculate specific pathogenic bacteria and then evaluate plant disease symptoms after a certain period of time, such as the size of leaf lesions, the number of lesions, or the overall disease resistance of the plant. The importance of data preprocessing is reflected in its ability to improve the accuracy and reliability of analysis. After phenotypic data are collected, normalization, removal of outliers, and consideration of the effects of environmental factors need to be performed to ensure the quality of the data. In addition, considering the complexity of corn disease resistance traits, multiple environments and repeated experiments are usually required to increase the stability and representativeness of the data. For example, Poland et al. (2011) in their study collected extensive phenotypic data on maize disease resistance through field trials and genotyped the samples using high-density SNP chips. By comprehensively analyzing phenotypic and genotypic data, they successfully identified multiple genetic markers associated with maize resistance to multiple diseases. Precise collection and careful processing of phenotypic data are the basis for conducting GWAS and successfully identifying genes associated with disease resistance in maize. By combining modern genetics and statistical methods, GWAS can reveal the genetic mechanism of disease resistance traits in corn and provide important genetic resources for breeding. 2.3 Association analysis and identification of candidate genes Genome-wide association studies (GWAS) is based on the principles of population genetics and identifies genes or gene regions associated with specific traits by analyzing the association between genetic variation and phenotypic traits. In the study of corn disease resistance, GWAS takes advantage of the rich genetic diversity of corn and identifies genetic markers related to disease resistance by analyzing the genotypes and phenotypes of a large number of individuals. The theoretical basis of this method is that the genetic variation of a trait can be detected through statistical correlation between loci and trait phenotypes, thereby revealing the underlying genetic mechanism (Flint-Garcia et al., 2005). The experimental design of GWAS usually involves the following key steps: First, select a maize population with high genetic diversity as the research object, which may be a natural population or a specifically constructed population, such as a multi-parent mixed population (MAGIC) or a combined population. Secondly, comprehensive genotyping of these individuals is performed, usually using high-throughput sequencing technology or gene chip technology to identify genetic markers such as single nucleotide polymorphisms (SNPs). The disease resistance phenotype of each individual is then accurately assessed, which may include field trials and artificial inoculation experiments. Finally, statistical models were applied to analyze the association between genotype and phenotype and identify genetic markers associated with disease resistance. After completing a GWAS, the results of association analysis usually appear as a series of genetic markers significantly associated with the disease resistance phenotype. The gene regions where these markers are located are candidate disease resistance gene regions. Like Poland et al. (2011), they successfully identified several key regions related to leaf rust resistance in corn through GWAS. Subsequently, the researchers further identified specific disease resistance genes within these regions through candidate gene mapping, expression analysis, and functional verification. The process of identifying and validating candidate genes includes several key steps: First, bioinformatics methods are used to predict candidate genes within associated regions and analyze the expression patterns and functional annotations of these genes. Next, these candidate genes are knocked out or knocked in in corn through transgenic or gene editing technology (such as CRISPR/Cas9), and their effects on disease resistance are observed. In addition, the disease resistance function of these genes can be verified through pathogen inoculation
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