对乌拉圭玉米品种进行分类,开发核心品种

M. Malosetti, T. Abadie
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引用次数: 0

摘要

建议利用核心种质资源提高种质资源的利用率。核心集合是缩小大小的子集,用来表示具有最小冗余的集合的多样性。由于多样性分布在不同组织程度的群体之间和群体内部,因此在选择核心集合之前,应将材料充分分类到相关的群体中。本研究比较了乌拉圭玉米种质资源的不同分类策略,并采用最佳分类策略选择核心种质资源。利用现有的玉米数据库,采用多变量方法比较了以下分类策略:i)种族分类,ii)地理来源(国家南部和北部),以及iii)籽粒类型和地理来源的组合。第三种选择被认为是最好的分类标准,因为它考虑到与多样性分布密切相关的两点:基因型组成和地理来源。在收集中确定了以下五个类群:a) Pop, b) Floury, e) Dent, d) Southern flint -semiflint和e) Northern flint -semiflint。共有90个品种入选乌拉圭玉米核心选集。每个组在核心中按其大小的对数比例表示。将Core中的17个变量的范围与整个集合中的变量的范围进行比较,以验证子集的代表性。平均而言,91.2%的区间保留在核心中,证实了其代表性。
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Classifying Uruguayan maize collection to develop a core collection
Core Collections were suggested to improve germplasm utilization. A Core Collection is a subset of reduced size, chosen to represent the diversity of a collection with a minimum of redundancies. Because diversity is distributed between and within groups with different degrees of organization, accessions should be classified adequately into related groups before the selection of a Core Collection. In this research, different classification strategies for the Uruguayan Maize Collection were compared, and the best was used to select a Core Collection. The following classification strategies were compared following a multivariate approach using the available maize data base: i) racial classification, ii) geographic origin (south and north of the country), and iii) a combination of kernel type and geographic origin. The third option was considered the best classification criterion, since it takes into account two points which are closely related to the distribution of diversity: genotypic composition and geographical origin. The following five groups were identified in the collection: a) Pop, b) Floury, e) Dent, d) Southern Flint-semiflints, and e) Northern Flint-semiflints. A total of 90 accessions were selected to constitute the Uruguayan Maize Core Collection. Each group was represented in the Core in proportion to the logarithm of its size. The ranges of 17 variables in the Core were compared with those in the entire collection to verify the representativeness of the subset. On average, 91.2% of the ranges were retained in the Core, confirming its representativeness.
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