Rice Genomics and Genetics 2024, Vol.15, No.2, 48-57 http://cropscipublisher.com/index.php/rgg 50 Figure 1 Expression levels of TM-1 and ZMS12 plants in different samples (Adopted from Fang et al., 2017) 3.2 Application of GWAS in plant genetics genome-wide association analysis (GWAS) in plant genetics has made significant progress and plays an important role in analyzing the genetic basis of complex traits, discovering new genes, and analyzing genetic variation. GWAS can help identify genes or genetic variants associated with target traits through large-scale genotypic and phenotypic data. In plant genetics research, these target traits may include important agronomic traits such as yield, quality, resistance, and stress tolerance. Currently, two commonly used technologies for detecting SNPs include gene chips and direct resequencing. Candidate genes or genetic variants of these traits can be accurately located through GWAS, providing an important reference for subsequent functional research and genetic improvement. GWAS can help resolve the genetic basis of complex traits. Many plant traits are jointly regulated by multiple genes, and each gene may only contribute to a small part of the trait. Through high-resolution analysis of the whole genome, GWAS can reveal the joint action modes and genetic mechanisms of these genes, helping us better understand the genetic basis of complex traits. GWAS can also discover new genes and genetic variants. Traditional genetic analysis methods often require prior knowledge or specific hypotheses, while GWAS can comprehensively explore the association between the genome and phenotype without restrictions, thereby discovering new genes and genetic variants. 3.3 Advantages and limitations of GWAS in rice genetics research Genome-wide association studies (GWAS) have become an important methodology in rice genetics research, which uses statistical means to find genetic variations associated with specific traits in the genome sequences of different individuals. This method has significant advantages in analyzing the genetic basis of complex traits. GWAS can identify genetic markers related to important rice traits on a genome-wide scale, such as yield, disease resistance, and stress tolerance. This is of great significance to rice breeding because it can help breeders quickly locate key genes that affect target traits. Compared with traditional genetic mapping methods, GWAS does not require a priori information about genes or traits, which enables it to discover new trait-related genes in unknown genetic backgrounds. Many rice traits, such as yield and disease tolerance, are complex traits controlled by multiple genes. GWAS can reveal the genetic mechanisms behind these complex traits and provide a powerful tool for understanding the genetic basis of traits (Yano et al., 2019). At the same time, GWAS also has many limitations. For example, GWAS may produce false positive results, that is, certain genetic markers that are not related to the trait are mistakenly considered to be related to the trait. This requires researchers to adopt strict statistical correction methods to reduce the occurrence of false positives. For less frequent genetic variants, GWAS may have difficulty detecting their association with traits because the effects of these rare variants may require very large sample sizes to be detected. The genetic background diversity of rice germplasm resources may affect the results of GWAS. If the sample contains individuals with widely varying
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