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观赏桃种质资源表型性状多样性评价
引用本文:张斌斌,蔡志翔,沈志军,严娟,马瑞娟,俞明亮. 观赏桃种质资源表型性状多样性评价[J]. 中国农业科学, 2021, 54(11): 2406-2418. DOI: 10.3864/j.issn.0578-1752.2021.11.013
作者姓名:张斌斌  蔡志翔  沈志军  严娟  马瑞娟  俞明亮
作者单位:1江苏省农业科学院果树研究所/江苏省高效园艺作物遗传改良重点实验室,南京 2100142南京林业大学风景园林学院,南京 210037
基金项目:农业部农作物物种资源保护项目(2016NWB007);江苏现代农业(桃)产业技术体系建设项目(JATS[2018]257);江苏现代农业(桃)产业技术体系建设项目(JATS[2019]401)
摘    要:【目的】探讨以质量性状和数量性状为依据对观赏桃种质资源多样性进行评价的可行性,为观赏桃种质资源评价和新品种选育提供参考。【方法】采集38份观赏桃种质的10个质量性状(花型、花瓣类型、花瓣颜色、雌雄蕊高度比、花粉育性、萼筒内壁颜色、花药颜色、叶色、树型、生长势)和6个数量性状(花径、节间长度、花芽/叶芽、单花芽/复花芽、花芽起始节位、生育期)数据,并通过赋值法对质量性状指标进行分级。在对所有表型性状进行相关性分析的基础上,对数据进行主成分分析和系统聚类分析。【结果】观赏桃种质数量性状的多样性指数、变异程度均较质量性状高。相关性分析发现这些性状间存在内在的关系,使得信息重叠。进一步进行主成分分析,将16个表型性状指标转化为10个主成分,前6个主成分可反映38份观赏桃种质表型性状的主要特征信息,其所包含的表型性状因子可以作为观赏桃种质创新和亲本选择的主要性状指标。对38份观赏桃种质16个性状的原始数据进行标准化转换,利用欧氏距离、运用离差平方和法进行系统聚类分析,在遗传距离为9附近将其聚为8个类群,发现直观表现特征最明显的树型、花瓣类型、叶色、生长势等是进行不同类群区分的主要表型性状。【结论】利用表型性状数据,以质量性状调查为依据,结合对数量性状的观测,分析观赏桃种质资源多样性并进行评价是可行的。

关 键 词:观赏桃  质量性状  数量性状  相关性分析  主成分分析  聚类分析  
收稿时间:2020-08-10

Diversity Analysis of Phenotypic Characters in Germplasm Resources of Ornamental Peaches
ZHANG BinBin,CAI ZhiXiang,SHEN ZhiJun,YAN Juan,MA RuiJuan,YU MingLiang. Diversity Analysis of Phenotypic Characters in Germplasm Resources of Ornamental Peaches[J]. Scientia Agricultura Sinica, 2021, 54(11): 2406-2418. DOI: 10.3864/j.issn.0578-1752.2021.11.013
Authors:ZHANG BinBin  CAI ZhiXiang  SHEN ZhiJun  YAN Juan  MA RuiJuan  YU MingLiang
Affiliation:1Institute of Pomology, Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Nanjing 2100142College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037
Abstract:【Objective】 The aim of this study was to investigate the feasibility of a phenotypic evaluation of the qualitative and quantitative characters present in the germplasm resources of ornamental peaches, and these findings could provide a scientific basis for the evaluation and selection of new varieties of ornamental peaches. 【Method】 Ten qualitative characters (flower type, petal type, petal color, height of pistil surface, pollen fertility, inter calyx tub color, anther color, leaf color, tree habit, and tree vigor) and six quantitative characters (flower size, internode length, flower bud/leaf bud, simple bud/multiple bud, site of first node with flower bud, and growth period) of 38 germplasm resources of ornamental peaches were investigated. The qualitative characters were graded by assignment method. After correlation analysis, the principal component analysis and cluster analysis of the phenotypic data were conducted. 【Result】 The diversity and degree of variation of quantitative characters in the germplasm resources were higher than that of the qualitative characters. The correlation analysis indicated that there was a relationship among some of the different characters, so that the information of the characters was overlapped. The principal component analysis showed that 16 characters were transformed into 10 principal components, and the first six principal components reflected the main phenotypic characters of 38 germplasm resources of ornamental peaches. The phenotypic factors contained in these principal components could be used as the main character indexes for germplasm innovation and parent selection for breeding ornamental peaches. The original data of 16 characters of 38 germplasm resources of ornamental peaches were standardized, and the cluster analysis was conducted using Euclidean distance and the sum of squares method. The germplasm resources were clustered into eight groups with a genetic distance of nine. The most obvious characteristics, such as petal type, leaf color, tree habit, and tree vigor, were the main phenotypic characters that could be used for distinguishing different groups. 【Conclusion】 This qualitative and quantitative study showed that it was feasible to analyze and evaluate the diversity of germplasm resources of ornamental peaches based on their phenotypic characters.
Keywords:ornamental peach  qualitative character  quantitative character  correlation analysis  principal component analysis  cluster analysis  
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