Field Crop 2024, Vol.7, No.1, 9-16 http://cropscipublisher.com/index.php/fc 10 complex genetic network requires researchers to adopt a more systematic approach in future studies. For example, GWAS and other omics data were integrated to fully analyze the molecular mechanism of barley resistance. Using GWAS to study the molecular genetic mechanism of barley resistance not only deepens the understanding of plant stress response mechanism, but also provides a new strategy for coping with global climate change and ensuring food security. With the advancement of genome sequencing technology and the development of bioinformatics tools, it is expected that more unknown resistance genes will be revealed in the future, providing a solid scientific foundation for the sustainable production of barley and other crops. 1 Overview of GWAS Technology 1.1 The fundamentals of GWAS technology Genome-wide association analysis (GWAS) techniques have become a central tool in modern genetics and genomics research, especially in revealing the genetic basis behind complex traits. The basic principle of GWAS is to analyze the association between genetic variants (especially single nucleotide polymorphisms, SNPs) and phenotypes through statistical methods in order to identify genes or genetic regions that influence specific traits. The advantage of this approach is its genome-wide analytical capability, which enables researchers to discover new relevant genetic markers without prior knowledge of the genetic control mechanisms of the target trait (Marees et al., 2018). The process of GWAS begins with precise measurements of the phenotypes and genotypes of a large number of individuals. Phenotypic data provide quantifiable information about studied traits, while genotypic data reveal the genetic variation of an individual across the whole genome, and use statistical methods to analyze these data to determine which genetic loci are significantly associated with phenotypic variation. These significantly correlated genetic loci are often considered candidate regions for influencing traits (Marees et al., 2018). Although the application of GWAS in genetic research has achieved remarkable results, the technology also faces a series of challenges. GWAS requires large sample sizes to ensure adequate statistical power because genetic control of traits often involves multiple genes, each of which may have a relatively small effect. Genetic markers discovered by GWAS often require confirmation of their function through further bioinformatic analysis and experimental validation, a process that can be complex and time-consuming (Figure 1). Figure 1 Challenges and opportunities in genome-wide association study (GWAS) (Cortes et al., 2021) 1.2 The development of GWAS technology The development of genome-wide association analysis (GWAS) was an important milestone in the field of modern genetics and genomics, marking a key step in scientists' efforts to parse the genetic basis of complex traits. Since its first successful application to human genetic research in 2005, GWAS technology has experienced rapid development and wide application, which has greatly promoted the understanding of genetic mechanisms of polygenic diseases, crop traits, and other complex traits (Figure 2) (Cortes et al., 2021).
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