International Journal of Molecular Evolution and Biodiversity, 2025, Vol.15, No.2, 99-110 http://ecoevopublisher.com/index.php/ijmeb 102 Breakthroughs in modern molecular biology techniques have revolutionized genetic research. High-throughput technologies such as genome-wide sequencing (GBS) and transcriptome sequencing can systematically detect SNP variations across the entire genome, greatly improving the accuracy of population genetic structure analysis. The latest research has successfully identified multiple population-specific genetic markers and functional sites related to ecological adaptability using these technologies (Shi et al., 2024b). These findings not only deepen our understanding of the genetic background of species, but also provide a molecular basis for the scientific protection and rational use of germplasm resources. 3.3 Methods for evaluating genetic diversity The evaluation of the genetic diversity of Linderae obesa mainly adopts a multi-dimensional index system, including genetic similarity coefficient, genetic distance matrix and systematic cluster analysis (Ye et al., 2017). Among them, the unweighted group average method (UPGMA) as a classic clustering algorithm can effectively divide the genetic groups of populations based on molecular marker data. The study using this method found that Linderae obesa populations in different ecological regions showed obvious genetic differentiation patterns (Gu et al., 2010). In order to accurately quantify the degree of genetic variation, researchers generally use parameter indicators such as Shannon information index and Nei gene diversity index. These indices convert genetic variation within populations into comparable numerical results through mathematical models. The measured data show that the Shannon index of Linderae obesa populations is generally higher than 0.3, which confirms that its genetic diversity is at a medium-to-high level (Bi, 2008). This kind of quantitative analysis provides a scientific basis for screening core germplasm resources with breeding value, and has important guiding significance for formulating effective protection strategies. 3.4 Genetic structure analysis The Qingchuan Linderae population exhibits unique genetic structural characteristics, which contains the dual codes of species evolution and environmental adaptation. AMOVA analysis reveals an important rule: genetic variation is mainly enriched within the population, while differences between populations are only secondary (Nakamura et al., 2021). This distribution pattern suggests that the intensity of gene exchange within the population is significantly higher than the genetic connection between geographically isolated populations. Further evaluation by Fst value showed that different populations showed moderate genetic differentiation. Such differentiation trends may be driven by ecological factors such as altitude differences, microenvironmental changes, and uneven soil characteristics. The results of gene flow parameters also showed that pollen dispersal and seed dispersal played a synergistic role in maintaining genetic connections between populations. In-depth analysis of these genetic characteristics has dual value. At the theoretical level, it provides a typical case for understanding the microevolution process of species; at the application level, it points out the direction for formulating scientific protection strategies. Maintaining the ecological diversity of the native habitat can not only maintain the survival of existing genetic variation, but also create favorable conditions for the sustainable development of Linderae resources (Xiong et al., 2020). This discovery organically combines genetic principles with conservation practices, providing new ideas for resource management of rare medicinal plants. 4 Screening and Functional Analysis of Superior Genes inLindera aggregata 4.1 Screening of functional genes related to medicinal ingredients The pharmacological effects of Lindera aggregata mainly depend on its unique secondary metabolite system, among which the active ingredients represented by volatile oils, phenols and flavonoids are particularly critical (Chao, 2003). With the help of high-throughput transcriptome sequencing technology, researchers systematically mined and identified multiple core functional genes that regulate the above metabolic pathways. In particular, in the MVA (mevalonic acid) and MEP (methylerythrose phosphate) metabolic pathways, some genes have been clearly identified to be closely related to the synthesis of volatile oils, providing a key basis for revealing the molecular mechanism of Lindera aggregataquality formation (Shi et al., 2024a).
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