Genomics and Applied Biology 2024, Vol.15, No.1, 12-21 http://bioscipublisher.com/index.php/gab 14 The genetic complexity of disease resistance in maize is an important part of agricultural genetics and breeding research. Traditionally, corn disease resistance is considered to be controlled by multiple genes, and these genes genetically exhibit the action mechanisms of major genes and pleiotropic genes. Major genes have a significant impact on traits and can produce large phenotypic effects at a single locus. In research on corn disease resistance, several major genes have been identified that play a decisive role in resisting specific pathogens. For example, certain genes specifically control resistance to specific diseases such as southern rust or leaf spot in corn. The discovery of these major genes provides the possibility of rapid screening and breeding of disease-resistant varieties, because breeders can directly select for these genes through molecular marker-assisted selection (MAS) (Benson et al., 2015). Unlike major effect genes, pleiotropic genes (or small effect genes) contribute less to traits, but due to their large number, their overall effect in the formation of traits cannot be ignored. The role of pleiotropic genes is reflected in their effect on traits through cumulative effects, which is particularly common in complex traits such as disease resistance. The identification and utilization of pleiotropic genes is a major challenge in breeding because their effects are affected by environmental factors and genetic interactions and are difficult to directly select through traditional breeding methods. Through GWAS, researchers can identify major genes and pleiotropic genes that affect corn disease resistance on a genome-wide scale. The advantage of GWAS is that it does not require a priori hypotheses to search for signals related to specific traits in large-scale genetic variations, allowing even those genes with small effects to be detected. In addition, GWAS can also reveal the complex interactions between genes and between genes and the environment, providing a more comprehensive perspective for understanding the genetic mechanism of disease resistance in maize. 1.3 Data analysis and interpretation Genome-wide association studies (GWAS) in the study of corn disease resistance is based on the systematic analysis of the relationship between large-scale genetic variation and phenotypic traits. The process involves complex data processing and analysis steps aimed at identifying genetic markers associated with disease resistance in corn. The data analysis pipeline typically begins with a genome-wide scan of high-density genetic markers (such as single nucleotide polymorphisms, SNPs), followed by the use of statistical models to identify associations between these markers and specific disease resistance traits. The data preprocessing stage includes quality control, such as removing low-quality SNPs and samples, and correcting for the potential impact of population structure and kinship on association analysis. This is because uncorrected population structure and kinship may lead to false positives in association analysis results. Then, association analysis usually uses linear mixed models (LMM) or generalized linear mixed models (GLMM). These models can simultaneously consider the influence of genetic background and environmental factors to improve the accuracy and stability of association analysis. For the interpretation of GWAS results, we usually focus on statistically significant SNPs whose P values are lower than a preset threshold, indicating that they are significantly associated with disease resistance traits in corn. However, due to the issue of multiple testing, these P values need to be adjusted through correction methods (such as Bonferroni correction) to reduce the incidence of false positives. Taking the southern rust resistance trait of corn as an example, Weng et al. (2011) successfully identified multiple SNPs significantly associated with rust resistance traits through GWAS analysis. Through further gene mapping and functional verification, the researchers confirmed candidate genes near these SNPs, providing important clues for a deeper understanding of the resistance mechanism of corn to southern rust. In addition, these genetic markers related to disease resistance provide valuable resources for molecular-assisted breeding and accelerate the breeding of highly disease-resistant corn varieties.
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