Tree Genetics and Molecular Breeding 2025, Vol.15, No.1, 9-17 http://genbreedpublisher.com/index.php/tgmb 11 2.3 Impact of ecological factors on genetic diversity Climatic conditions such as temperature and precipitation can affect the growth performance and adaptability of plants, leading to genetic differentiation in Sapindus mukorossi populations in different regions (Diao et al., 2014; Liu et al., 2021d; Wang et al., 2022). Liu et al. (2021b) and Liu et al. (2021c) hold that the pH value and moisture content of the soil can affect the growth status of Sapindus mukorossi, and these differences are also directly related to the genetic variations of populations in different regions. Sun et al. (2018) and Liu et al. (2022) found that geographical isolation is equally important and it may cause long-term differentiation among populations in different regions. 3 Methods and Techniques for Genetic Diversity Analysis 3.1 Research materials and sampling strategy The studies of Mahar et al. (2011), Ba (2014), and Liu et al. (2022) all found that Sapindus plants are distributed in many places (such as China, India, etc.), and there are significant genetic differences among the populations in these regions. Extensive sampling is beneficial for breeders and conservationists to better grasp germplasm resources and lay the foundation for subsequent selection and breeding as well as resource conservation. Liu et al. (2021a) demonstrated that the phenotypic trait data of Sapindus mukorossi were collected through agricultural morphology methods, and the assessment contents included the size of the fruit, the oil content in the seeds, and the saponin level in the fruit, etc. These traits will be recorded first, and then the outstanding germplasm resources will be identified through correlation analysis and principal component analysis. Sun et al. (2018a) found that the research would also take into account the phenotypic plasticity and adaptability of Sapindus mukorossi, as these factors would also affect its economic value. 3.2 Application of molecular marker technologies SSR and SNP markers can detect very subtle genetic differences when analyzing the genetic diversity of Sapindus mukorossi. SSR markers are often used to evaluate the genetic diversity and population structure of Sapindus mukorossi. Studies have found that they can reveal obvious intraspecific and interspecific variations. Sun et al. (2018b) and Liu et al. (2022) hold that the advantages of such markers lie in their codominant nature, large amount of information, high polymorphism, and suitability for studying kinship and genetic diversity. High-throughput sequencing technology can be used to obtain more comprehensive genetic information. It is useful for in-depth understanding of the changes in the entire genome, identifying those candidate genes related to agronomic traits, and revealing the evolutionary history of the Sapindus mukorossi. Xue et al. (2022) demonstrated that the introduction of high-throughput sequencing can provide a more detailed analysis of genetic differences among germplasm resources and more accurately select superior materials suitable for breeding. 3.3 Methods for evaluating genetic diversity Statistical indicators such as the Shannon index and the Nei index are beneficial for understanding the genetic differences within and between populations. The Shannon information index is often used to measure the genetic diversity of different populations. The research of Mahar et al. (2011b) shows that Sapindus mukorossi in different geographical regions has significant changes in polymorphism and genetic differences. Sun et al. (2018a) found that the Nei genetic diversity index is often used to quantitatively assess the degree of variation and has been used to analyze the diversity parameters of Sapindus plants in some studies based on ISSR markers. Methods such as UPGMA cluster analysis and Dice genetic similarity coefficient are often used to analyze the genetic similarity and distance between germplasms. Mahar et al. (2011b) and Ba (2014) found that UPGMA clustering can divide different populations into several groups based on genetic distance, and the results are often related to geographical distribution. That is to say, populations from similar areas are more likely to be classified into one category. These analytical methods are very useful for understanding the genetic relationships and differences among different germplasm resources.
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