Maize Genomics and Genetics 2024, Vol.15, No.1, 1-8 http://cropscipublisher.com/index.php/mgg 3 2 Introduction to Genome-wide Association Analysis (GWAS) Genome-wide association analysis (GWAS) is a method widely used in genetic research to identify genetic markers that influence a specific trait by analyzing the association between genetic variation in the genome and the trait. The emergence of GWAS marks a new era in genetic research, especially in the field of plant genetics, which provides a powerful tool for analyzing the genetic basis of complex traits. 2.1 Principles of GWAS and its application in plant genetics The basic principle of GWAS is to use genomic data of a large number of individuals (Uffelmann et al., 2021) to find those single nucleotide polymorphisms (SNPs) that are significantly associated with phenotypic variation of specific traits through statistical analysis. These SNPs may be located in the coding region of the gene or in the regulatory region, affecting gene expression. In plant genetics research, GWAS is widely used to analyze important agronomic traits of crops, such as yield, disease resistance, quality, etc. Compared with traditional genetic analysis methods, GWAS can directly identify genetic variations related to traits across the entire genome without knowing the gene location in advance, greatly improving the efficiency and accuracy of genetic research (Cortes et al., 2021). Through GWAS, researchers can identify key genetic factors affecting specific traits in natural populations with rich genetic diversity (Figure 1), which is of great significance for the genetic improvement of crops and the cultivation of new varieties. For example, through GWAS analysis, scientists have successfully identified multiple key genes that affect corn grain quality, wheat disease resistance, and rice yield. These findings not only enrich our understanding of the genetic mechanisms of these traits, but also provide information for crop breeding. new target. Figure 1 Different genome-wide association study methods ask different questions 2.2 Comparison of GWAS and traditional genetic marker association studies Although GWAS has shown its advantages over traditional genetic marker association studies in many aspects (Brachi et al., 2011), there are also certain complementarities between the two in practical applications. Traditional genetic marker association studies, such as linkage analysis, usually look for markers related to traits by analyzing genetic data of specific groups (such as families or inbred lines) in a limited genetic background. This method relies on genetic linkage and is therefore somewhat limited in positioning accuracy, especially in the study of complex traits. In contrast, because GWAS is conducted in a wide range of natural populations, it can take advantage of the large number of genetic recombination events accumulated in natural populations to accurately locate genetic variations associated with traits across the entire genome. In addition, GWAS can also reveal the interaction of multiple genes in the formation of traits, making it possible to understand the genetic complexity of traits. However, GWAS also has its limitations, such as the high requirements on population structure and degree of association, and the so-called "missing heritability" problem, that is, GWAS sometimes cannot explain all genetic variation. 3 Application of GWAS in the Study of Corn Grain Quality Traits Genome-wide association analysis (GWAS), as a powerful genetic research tool, has been widely used in the study of corn grain quality traits. It provides an efficient method to reveal the genetic basis that affects corn quality traits by analyzing the association between genetic variation and trait phenotypes.
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