Medicinal Plant Research 2025, Vol.15, No.3, 99-109 http://hortherbpublisher.com/index.php/mpr 103 4 Genotypic Diversity Analysis Based on Molecular Markers 4.1 Selection of molecular marker types In the genetic diversity analysis of the Sapindus genus, SSRS, ISSRs, and SNPS, are the most widely used types of molecular markers. Studies have shown that the EST-SSR marker is highly effective in revealing the high genetic diversity and population structure of Sapindus sapindus germplasm (Liu et al., 2022). ISSR markers have also been successfully used to detect genetic variations and can link specific loci with important traits of fruits (kernel oil content, saponin content, etc.) (Sun et al., 2019). Although the application of SNP markers in Sapindus research is not as common as the former two, they have gradually been widely used in plant genomics due to their high resolution and abundant quantity (Nam et al., 2021; Xue et al., 2022). Both SSR and ISSR markers show advantages such as high polymorphism and large information volume, which are suitable for differentiating related germplasm materials and providing support for the construction of core germplasm banks (Liu et al., 2022). Among them, SSR markers are valued for their good repeatability and co-dominant genetic characteristics, while ISSR markers are widely adopted because they can generate a large number of loci under the condition of fewer DNA samples (Sun et al., 2019; Nam et al., 2021). 4.2 DNA extraction and genotyping protocols Samples are usually collected from the leaf tissues of healthy and mature plants, and then stored at low temperatures to ensure the integrity of DNA. DNA extraction is generally carried out using standard plant genomic DNA extraction kits or CTAB-based methods, and high-quality DNA suitable for PCR labeling analysis can be obtained (Sun et al., 2019; Nam et al., 2021). PCR amplification is carried out using specific primers targeting the SSR or ISSR regions. The amplification products were then separated by agarose gel or polyacrylamide gel electrophoresis, and the band patterns were revealed using staining agents. The scoring method of ISSR markers is to record the presence or absence of bands, while SSR markers are judged by allele size, thereby generating a data matrix of binary or polyallelic genotypes to provide a basis for subsequent analysis (Liu et al., 2022). 4.3 Genetic diversity and population structure analysis Genetic distance and similarity coefficient can be calculated by using labeled data. Commonly used indicators include Nei genetic distance and Jaccard similarity coefficient, which are used to quantify the genetic relationship between different materials (Sun et al., 2019; Liu et al., 2022). Cluster analysis methods, such as UPGMA and principal coordinate analysis (PCoA), are often used to group materials based on genetic similarity. The population structure was further analyzed through model inference methods. The results indicated that there was significant differentiation within different species and their populations (Liu et al., 2022). For instance, S. mukorossi and S. delavayi can be clearly classified into different genetic populations, and establishing a core germplasm bank can cover genetic diversity to the greatest extent, providing a solid foundation for conservation and breeding (Sun et al., 2019). 5 Correlation Analysis Between Phenotypic Traits and Genotypic Diversity 5.1 Methodologies for correlation analysis Canonical correlation analysis (CCA) is a multivariate statistical method, which is used to explore the relationship between two groups of variables. In the research of Sapindus and related plant germplasm, CCA has been applied to link environmental and phenotypic data, to reveal how multiple traits change cooperatively with genetic background and environmental gradient (Sun et al., 2017). For instance, CCA can identify which environmental factors have the greatest impact on the expression of key fruit and seed traits, thereby providing an overall perspective on the genotype-phenotypic-environment interaction. The Mantel test is widely used to evaluate the correlations, between genetic distance matrices (based on molecular markers) and phenotypic distance matrices (based on trait data), etc. This method is mainly used to help determine that, whether genetic diversity can be reflected in observable trait variations (Sun et al., 2018; 2019; Verma et al.,
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