Molecular Plant Breeding 2024, Vol.15, No.2, 52-62 http://genbreedpublisher.com/index.php/mpb 55 strategy does not rely on a priori genetic knowledge and can reveal the genetic basis of trait formation without knowing the candidate genes. The basic principle of GWAS is based on the concept of genetic linkage disequilibrium (LD), that is, certain genetic markers (such as SNPs) tend to be inherited together due to limitations of historical recombination events. When a SNP is in the same LD block as a gene that has a greater influence on the trait, the frequency change of the SNP may be associated with the variation of the trait (Mores et al., 2021). By detecting SNPs that are significantly associated with a trait, researchers can determine the underlying genetic basis of the trait and guide subsequent functional research and breeding efforts. The implementation steps of GWAS usually include sample collection, genotype determination, statistical analysis and result verification. First, a sufficient number of individuals with or without a specific trait are collected as a research sample. Then, high-throughput sequencing or gene chip technology is used to determine the genome-wide SNPs of these samples. Afterwards, statistical methods (such as linear regression analysis) are used to evaluate the strength of the association between each SNP and the trait. Finally, the biological significance of key SNPs was verified through functional experiments. A typical example of GWAS is its application in the genetics of human disease, such as searching for genetic variants associated with type 2 diabetes. By analyzing genetic data from thousands of patients and healthy controls, the researchers were able to identify multiple SNPs associated with increased disease risk. These findings not only provide new insights into disease mechanisms, but also provide the possibility for the development of new treatments and preventive strategies. Although GWAS is a powerful tool, it also faces some challenges and limitations. First, due to genetic linkage disequilibrium, the SNPs identified by GWAS may not be causal variants that directly lead to trait variation, but are located within the same LD block as the genes that actually affect the trait. Second, for complex traits, small effects of multiple genes may be difficult to detect. In addition, large-scale GWAS require large amounts of samples and expensive genotyping costs, although the development of high-throughput sequencing technologies is lowering these barriers (Elena and Giménez, 2021). GWAS provides an effective strategy for analyzing the genetic basis of complex traits and has been widely used in many fields such as human diseases and crop trait improvement. With the advancement of gene sequencing technology and the development of data analysis methods, the accuracy and efficiency of GWAS will be further improved, providing more support for biomedical research and precision agriculture. 3.2 Comparison of GWAS and traditional breeding methods Genome-wide association studies (GWAS) and traditional breeding methods each have their own unique applications and advantages in crop improvement. Traditional breeding relies on phenotypic observation and selection, using known genetic variations to improve crop traits, such as drought tolerance and disease resistance. This approach relies on the complex interaction of the phenotypic expression of the trait and the genetic background, often requiring multiple generations of selection to ensure stable expression of the desired trait. Although traditional breeding technology has achieved remarkable achievements historically, it has its limitations, especially in improving the efficiency and precision of crop traits (Elena and Giménez, 2021). With traditional breeding methods, GWAS provides a powerful tool to identify genes or genetic markers that influence traits by analyzing the association between genetic variation in the crop genome and specific traits. GWAS uses molecular markers, such as single nucleotide polymorphisms (SNPs), to explore the connection between genes and traits. The advantage of this method is that it can cover the entire genome and identify precise genetic locations related to traits, thereby providing a scientific basis for molecular breeding of crop traits. Compared with traditional breeding, GWAS has obvious advantages in understanding the genetic basis of traits and improving the accuracy of selection (Elena and Giménez, 2021).
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