International Journal of Molecular Evolution and Biodiversity, 2025, Vol.15, No.2, 99-110 http://ecoevopublisher.com/index.php/ijmeb 104 Figure 2 Map of mitochondrial genome assembly and annotation of L. aggregata (Adopted from Shi et al., 2024b) Image caption: (A) The map of the preliminary assembly of the mitochondrial genome; (B) The master circle and linear fragment map of mitochondrial genome; Each contig is marked with different number, and the numbers order of connection is the order of unlocking circles; (C) The map of mitochondrial genome annotation; Genes with different functions are described in different colors. The colored parabola in the center circle represents the dispersed repeats (Adopted from Shi et al., 2024b) In-depth research found that genetic variation also affects the accumulation level of total phenolic and flavonoid compounds - these substances are the material basis of the antioxidant activity of Linderae obesa. By integrating phenotypic and genomic data, researchers not only elucidated the genetic regulatory network of key metabolites, but also provided a molecular basis for the selection of new varieties with high medicinal value (Gu et al., 2010). This gene-trait association study has opened up a new path for the quality improvement of Linderae obesa. 5 Screening and Evaluation of Superior Lindera aggregata Germplasm in Qingchuan County 5.1 Comprehensive evaluation indicators of germplasm resources The evaluation of Qingchuan Linderae germplasm resources adopts a three-level indicator system, which systematically integrates characteristic parameters at the three levels of phenotype, genetics and function. The phenotypic level focuses on key indicators such as growth dynamics (plant height increment, number of branches), organ morphology (leaf characteristics) and medicinal components (volatile oil, phenolic and flavonoid content). These traits show continuous variation among different geographical populations, providing an intuitive basis for preliminary screening (Gu et al., 2010). Molecular marker technology has injected a new dimension into the evaluation system. SSR and SNP markers combined with statistical methods such as Shannon diversity index can accurately quantify genetic differences between germplasms. At the same time, functional gene expression profile analysis can effectively identify precious resources with special metabolic potential or stress resistance characteristics (Ye and Li, 2019). This "morphology-genetics-function" trinity evaluation framework not only significantly improves the screening efficiency, but also ensures the scientificity and reliability of the evaluation results. 5.2 Screening of germplasm with superior multi-traits Multivariate statistical methods provide powerful analytical tools for the screening of superior germplasm of Linderae. Methods such as principal component analysis (PCA) and hierarchical clustering can effectively integrate multidimensional trait data and identify superior germplasm with outstanding comprehensive traits from
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