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Table 5_Genetic diversity and core collection of Polygonati Rhizoma in China via SSR markers.xls

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NIAID Data Ecosystem2026-05-10 收录
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https://figshare.com/articles/dataset/Table_5_Genetic_diversity_and_core_collection_of_Polygonati_Rhizoma_in_China_via_SSR_markers_xls/30555353
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IntroductionPolygonati Rhizoma is a Traditional Chinese medicine derived from three species of the Polygonatum Mill. (Polygonatum kingianum, Polygonatum sibiricum, and Polygonatum cyrtonema), renowned for its culinary and medicinal uses. Despite extensive research, comprehensive studies on the population genetic diversity and core collection construction of medicinal Polygonatum species remain scarce. MethodsTo address this, we employed 18 highly polymorphic SSR markers to develop two machine learning models for species discrimination. Subsequently, we performed comprehensive population genetic analyses on 175 accessions, followed by core germplasm construction. ResultsThe study demonstrated that the machine learning-based approach achieved consistently high discrimination accuracy, exceeding 81% for Polygonatum species identification. Among the four investigated Polygonati Rhizoma, significant variations in genetic diversity were observed. Cluster and population structure analyses identified three primary subgroups. A core collection was constructed through stepwise clustering based on genetic distance. The C78 primary core collection achieved an allele retention rate of 84.59%, with minimal genetic redundancy. DiscussionThese findings provide a robust foundation for the conservation of medicinal Polygonatum spp. germplasm and offer potential resources for future genetic improvement and variety selection.

引言 黄精(Polygonati Rhizoma)是一种药食同源的中药材,其基原来源于黄精属(Polygonatum Mill.)的3个物种:滇黄精(Polygonatum kingianum)、黄精(Polygonatum sibiricum)及多花黄精(Polygonatum cyrtonema),兼具食用与药用价值。尽管当前相关研究已较为广泛,但针对药用黄精属物种的群体遗传多样性及核心种质构建的系统性研究仍较为匮乏。 材料与方法 为填补这一研究空白,本研究采用18个多态性丰富的简单序列重复(Simple Sequence Repeat,SSR)标记,构建了2个用于物种鉴定的机器学习模型。随后,对175份种质材料开展了全面的群体遗传分析,并完成了核心种质的构建工作。 结果 本研究结果表明,基于机器学习的物种鉴定方法始终保持较高的识别精度,黄精属物种鉴定准确率均超过81%。在本次研究所涉及的4份黄精种质中,其遗传多样性存在显著差异。聚类分析与群体结构分析共鉴定出3个主要亚群。本研究通过基于遗传距离的逐步聚类法构建了核心种质库,其中C78初级核心种质库的等位基因保留率达84.59%,且遗传冗余度极低。 讨论 本研究结果为药用黄精属种质资源的保护提供了坚实的理论基础,同时也为后续的遗传改良与品种选育提供了潜在的优异种质资源。
创建时间:
2025-11-06
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