Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 2 1 Principles and Methods of GWAS 1.1 Definition and principle of GWAS Genome-wide association study (GWAS) is a genetic method used to study the relationship between genetic variation and phenotypic traits. Its principle is to explore the association between genetic variation (such as single nucleotide polymorphisms, SNPs) and specific traits through statistical analysis based on genotype and phenotypic data of a large number of individuals (Uffelmann et al., 2021). This analysis can reveal the role of genetic variation in the expression of traits and provide clues for understanding the genetic basis of traits. The key of GWAS is to conduct high density genotyping of a large number of samples to cover the variation information of the whole genome, and to evaluate the correlation between each genetic variation site and the trait phenotype through statistical tests (such as linear regression analysis). If the frequency of a mutation site is significantly different in different phenotype populations, it is considered that the site is associated with the trait phenotype. GWAS can fully explore the genome without relying on prior knowledge, reveal the polygenic genetic basis of complex traits, and provide molecular markers for breeding. 1.2 The main steps of GWAS Genome-wide association study (GWAS) is a widely used genetic tool in crop disease resistance breeding, which reveals genetic markers and genes associated with traits by analyzing the association between genetic variation and phenotypic traits. The main steps of GWAS include sample collection and genotyping, phenotypic data collection, association analysis, result validation, and biological interpretation (Figure 1). It requires collecting a sufficient number of samples and conducting genotyping to obtain genome-wide genetic variation information. The phenotypes of the samples were then recorded in detail (Belzile and Torkamaneh, 2022). These data will be used for subsequent association analysis, which will then use statistical methods to analyze the association between genotype and phenotypic data to identify genetic markers associated with specific traits. Figure 1 The steps for conducting GWAS (Uffelmann et al., 2021) The identified associated sites or candidate genes need to be verified through independent sample sets or functional verification experiments. Finally, the biological interpretation of the identified associated sites or candidate genes is carried out to explore their roles and mechanisms in phenotype formation. Through these steps, GWAS can provide valuable genetic information for crop disease resistance breeding.
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