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植物研究 ›› 2015, Vol. 35 ›› Issue (5): 730-734.doi: 10.7525/j.issn.1673-5102.2015.05.015

• 论文 • 上一篇    下一篇

杜鹃红山茶叶片主要性状的遗传多样性分析

徐斌1;彭莉霞2;杨会肖1*;潘文1;张方秋1   

  1. 1.广东省林业科学研究院,广州 510520;
    2.广东生态工程职业学院,广州 510520
  • 出版日期:2015-09-15 发布日期:2015-11-20
  • 基金资助:
     

Genetic Diversity Analysis for Leaf Main Traits of Camellia azalea

XU Bin1;PENG Li-Xia2;YANG Hui-Xiao1*;PAN Wen1;ZHANG Fang-Qiu1   

  1. 1.Guangdong Academy of Forestry,Guangzhou 510520;
    2.Guangdong Eco-engineering Vocational College,Guangzhou 510520
  • Online:2015-09-15 Published:2015-11-20
  • Supported by:
     

摘要: 为有效评价和利用杜鹃红山茶基因资源、挖掘其优良性状,以37个杜鹃红山茶无性系为研究材料,对14个叶片的表型性状进行测定,分析各性状的变异系数、不同性状间的相关关系,并进行主成分分析和聚类分析研究。结果显示:14个叶片表型性状的变异系数为5.30%~47.00%,平均变异系数为18.52%,表明杜鹃红山茶叶片主要数量性状的变异较大,遗传多样性较丰富。叶长与叶形指数间的相关性系数达0.967,叶面积与叶片干重的相关性系数为0.942,叶柄长和叶柄长/叶长的相关性系数为0.828。14个性状可以综合为5个主成分,前5个主成分累计贡献率达80%,表明这些性状具有较强的代表性。根据系统聚类将37个杜鹃红山茶无性系划分为5个组。

关键词: 杜鹃红山茶, 变异系数, 相关分析, 主成分分析, 聚类分析

Abstract: In order to develop elite genotypes of Camellia azalea, we determined phenotypic traits based on 14 quantitative leaf traits from 37 samples, and analyzed the variation coefficient, correlations between traits, principal component analysis and cluster analysis. The variation coefficients of the traits ranged from 5.3% to 47%, and the average variation coefficient was 18.52%. The quantitative leaf traits of C.azalea resources had extensive variation with rich genetic diversity. The correlation coefficients between leaf length and leaf shape index, leaf area and leaf dried weight, and piteous length and piteous length/leaf length ratio were 0.967, 0.942 and 0.828, respectively. By principal component analysis indicated six leaf traits (leaf area, leaf length, piteous length/leaf length, left leaf width/leaf width, and leaf area/leaf dried weight) were the most important characteristics discriminating variation among 37 C.azalea samples. By hierarchical cluster analysis, 37 samples were divided into five groups.

Key words: Camellia azalea, variation coefficient, correlation analysis, principal component analysis, cluster analysis

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