Triticeae Genomics and Genetics, 2024, Vol.15, No.1, 19-30 http://cropscipublisher.com/index.php/tgg 20 This study will comprehensively review the progress and challenges in the GWAS discovery of stress tolerance trait genes in wheat crops over the past decade, and discuss the current research status and future development directions in this field. Through the analysis and summary of relevant literature and research results, it aims to provide scientific basis and theoretical support for the genetic improvement of stress tolerance of wheat crops and contribute to solving global food security issues. 1 Application of GWAS in the discovery of stress tolerance trait genes in wheat crops 1.1 Principles and methods of GWAS Genome wide association analysis (GWAS) is to detect the genetic variation (marker) polymorphisms of multiple individuals across the entire genome to obtain genotypes, and then combine the genotypes with observable traits, that is, Phenotype, perform statistical analysis at the population level, screen out the genetic variations (markers) most likely to affect the trait based on statistics or significant p- values, and mine genes related to trait variation. GWAS is a method of identifying genetic variants associated with specific traits or diseases by comparing genotypic and phenotypic data in large sample populations. Researchers need to collect genotype data from large sample populations. This is usually achieved through gene chips or the latest sequencing technology. Genotype data includes the SNPs on the genome of the samples participating in the study (Single Nucleotide Polymorphism) Information. At the same time, researchers also need to collect phenotypic data on the sample population participating in the study, that is, the manifestation of the trait or disease of interest. These phenotypic data may cover many aspects such as biology, physiology, and behavior. Before conducting GWAS, the collected genotypic and phenotypic data need to be cleaned and quality controlled. This includes steps such as removing missing values, correcting genotype bias, and excluding population structures to ensure the accuracy and reliability of the data (Xia et al., 2019). Next, the researchers used statistical methods to analyze the association between genotype and phenotype. In GWAS, commonly used correlation analysis methods include chi-square test, linear regression model, logistic regression model, etc. Through these methods, researchers can determine the relationship between genotypic variation and traits or diseases. Since GWAS involves a large number of SNP-trait comparisons, multiple testing correction is required to reduce the risk of false-positive results. Commonly used correction methods include Bonferroni correction, FDR (false discovery rate) correction, etc. Researchers need to interpret and functionally annotate discovered associations. This may involve research on the biological function of the gene itself, pathway analysis, expression regulation, etc., to gain a deeper understanding of the relationship between genotypic variation and traits or diseases. 1.2 Current status of GWAS application in wheat crops GWAS The application of (genome-wide association analysis) in wheat crops has made a series of important progress, providing strong support for revealing the stress tolerance trait genes and related genetic mechanisms of wheat crops. GWAS has been widely used to discover key genes affecting disease resistance and stress resistance traits of wheat crops. In wheat, researchers used GWAS to identify SNP markers and genes related to resistance to important diseases such as stripe rust and head blight, providing important genetic resources for wheat disease resistance breeding. GWAS has also made important progress in studying the adaptation mechanisms of wheat crops to drought, high temperature, salinity and other stress stresses. By analyzing large-scale genotypic and phenotypic data, researchers discovered multiple candidate genes related to stress response, revealing a complex network of stress response pathways. For example, Wang et al. (2020) tried to use TASSEL5.0 software to combine the whole panicle germination rates of 207 wheat varieties (lines) with 16 typing genes screened by 90K SNP chips. Genome-wide association
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