Bioscience Method 2024, Vol.15, No.1, 8-19 http://bioscipublisher.com/index.php/bm 9 Specifically, the application of GWAS allows researchers to discover novel, advantageous genetic variants in a broad range of crop populations that may have been overlooked in traditional breeding. For example, in major food crops such as rice and wheat, the application of GWAS has successfully identified multiple key genes or gene regions related to yield, disease resistance, and stress tolerance. These findings not only enrich our understanding of crop genetic diversity, but also provide new strategies for molecular-assisted selection and genetic improvement of crops. In addition, GWAS can also help to discover valuable genetic resources in wild species and local varieties, which are crucial for enhancing crop adaptability and sustainable production. Although GWAS has shown great potential in revealing crop genetic diversity, it also faces many challenges during its application, including data complexity, selection of analysis methods, interpretation of results, and effective use of genetic information. Therefore, future research needs to innovate and improve at multiple levels to fully leverage the role of GWAS in crop genetic diversity research and contribute to global agricultural production and crop improvement. 1 Overview of GWAS Technology 1.1 Basic principles and methods of GWAS Genome-wide association studies (GWAS) are a method used to find genes or genomic regions in genetic material that are associated with specific traits. The basic principle is based on a hypothesis: if a genetic variation (usually a single nucleotide polymorphism, SNP) is closely related to a specific trait, then individuals with this variation should show a certain degree of improvement in this trait. Common feature. GWAS identifies genetic variation associated with a trait by comparing the frequency of genetic markers in individuals or populations with different trait expressions. The GWAS method usually includes several key steps , which is to collect a large enough sample population that has obvious phenotypic differences in specific traits; then conduct a genome-wide scan on these samples to record thousands of Information about genetic markers (mainly SNPs); finally, statistical methods are used to analyze the correlation between these genetic markers and traits to identify markers that are significantly associated with traits (Hasan et al., 2021). During the analysis process, the statistical model used in GWAS can control the potential confounding effects of population structure and genetic background, thereby improving the accuracy of the association signal. This step is critical because population structure (i.e., differences in genetic background) can lead to false-positive results. Once SNPs that are significantly associated with a trait are identified, researchers can further explore the genes near those SNPs to identify specific genes or genomic regions that may have an impact on trait expression. A major advantage of GWAS is that it does not rely on prior genetic knowledge and enables unbiased exploration across the entire genome. This means that GWAS can reveal previously unrecognized new genes and genetic mechanisms that influence complex traits. However, the identified genetic markers usually require further experimental and functional studies to verify their actual impact on traits, which includes the use of genetic engineering, gene editing technology, and phenotypic identification (Peng et al., 2022). By integrating genetic and phenotypic data from large samples, GWAS provide a powerful method for understanding the genetic basis of complex traits. Despite challenges such as large data volumes, complex analyses, and difficult interpretation of results, GWAS has achieved remarkable achievements in multiple fields, especially in human disease genetics, agriculture, and plant breeding. 1.2 GWAS data types and acquisition methods To conduct genome-wide association studies (GWAS) mainly include two categories: genetic data and phenotypic data. Genetic data involves an individual's genomic information, usually in the form of single nucleotide polymorphisms (SNPs), while phenotypic data is about an individual's performance on specific traits, such as height, yield, disease resistance, etc. Accurate collection and high-quality processing of these two types of data are critical to the success of GWAS.
RkJQdWJsaXNoZXIy MjQ4ODYzMg==