International Journal of Horticulture, 2024, Vol.14, No.2, 66-77 http://hortherbpublisher.com/index.php/ijh 67 but have also identified key genetic factors affecting nutritional components such as lycopene content (Rodriguez et al., 2020). This study aims to analyze in depth how GWAS impacts the yield and nutritional value of vegetable crops and explores how to optimize vegetable crop traits from a genetic perspective. The main focus is on the key genetic markers identified using the GWAS method and how to apply these genetic findings to the breeding process to promote the improvement of vegetable crop traits. Through this study, we hope to fill the gap in the existing literature regarding the practical application and evaluation of using GWAS for genetic improvement of vegetable crops. Additionally, the study aims to provide insights into the future direction of crop genetic improvement, particularly on how to effectively utilize genetic resources to address global food supply challenges. 1 Overview of Genome-Wide Association Studies (GWAS) 1.1 Principles and workflow of GWAS Genome-Wide Association Studies (GWAS) is a population genetics-based method aimed at identifying associations between genetic variations and specific traits (Yoon et al., 2018). The core of GWAS is to analyze the genetic data (typically single nucleotide polymorphisms, SNPs) and phenotypic data (such as yield, disease resistance, nutritional components, etc.) of a large number of individuals to find which genetic variations are associated with the target trait. The workflow of Genome-Wide Association Studies (GWAS) includes several key steps. First, a sample population with genetic diversity needs to be selected, and its whole-genome data obtained through high-throughput sequencing techniques to ensure that the samples cover sufficient genetic variation. Subsequently, the phenotypic data of each sample, such as plant height, fruit size, nutrient content, etc., is recorded in detail to prepare for the association analysis. Using biostatistical methods, such as association analysis, the genetic markers (e.g., single nucleotide polymorphisms, SNPs, or other forms of genetic variation) that are significantly associated with the target trait are determined. Based on these statistically significant genetic markers, candidate genes that may affect the specific trait are identified. Finally, through experimental methods such as gene knockout or overexpression studies, the actual impact of these candidate genes on the trait is verified to ensure that the discovered associations have biological significance (Cirillo et al., 2018). 1.2 History and development of GWAS in plant science The development of Genome-Wide Association Studies (GWAS) in plant science has gone through several important stages. GWAS was initially used for genetic research on human diseases, such as heart disease, diabetes, autoimmune diseases, and mental disorders, proving its value in elucidating the genetic basis of complex traits (Abdellaoui et al., 2023). In the early 2000s, GWAS techniques began to be applied to plant science research (Figure 1), marking the expansion of its application scope (Fang and Luo, 2018). Figure 1 GWAS-related publications for different species of 2010-2018 (Adopted from Fang and Luo, 2018) Image caption: Publications were selected from the Web of Science (Adopted from Fang and Luo, 2018)
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