机构地区:[1]湖南农业大学,茶学教育部重点实验室,长沙410128 [2]国家植物功能成分利用工程技术研究中心,长沙410128 [3]柳州市林业科学研究所,柳州545300 [4]柳州市农业技术推广中心,柳州545002 [5]融水县汪洞乡农业技术推广站,柳州545300 [6]融水县农业局,柳州545300
出 处:《分子植物育种》2019年第16期5488-5503,共16页Molecular Plant Breeding
基 金:广西壮族自治区科技攻关项目(桂科攻1598006-5-3);湖南省研究生科研创新项目(CX2016B284)共同资助
摘 要:本研究以广西柳州市九万山片区的古茶树资源为研究对象,通过对74份古茶树资源的树型、树姿及叶片等19个形态性状进行调查,并根据这些性状数值进行形态多样性和相关性的统计分析,进而构建系统聚类树状图来研究这74份资源的亲缘关系及变异规律。结果表明,13个质量性状的多样性指数在0.57~1.59之间,平均为1.06,变异系数在23.11%~72.87%之间,平均为38.36%;6个数量性状的多样性指数在1.61~2.08之间,平均为1.95,变异系数在9.59%~29.21%之间,平均为15.74,表现出丰富的遗传变异。以皮尔森(Pearson)相关系数对74份古茶树资源的19个形态学性状进行相关性分析,结果显示呈极显著正相关的有18对性状,呈显著正相关的有7对性状,呈极显著负相关的有5对性状,呈显著负相关的有6对性状。对17个叶片表型性状进行主成分分析,提取出6个主成分,其累积贡献率达75.868%,它们包含了原始变量的绝大部分信息,根据各形态性状与各主成分之间的相关性大小,筛选出了一些对叶片形态影响最大的性状,淘汰了少数参考价值不大的性状,可作为今后品种选育和分类研究的主要形态性状。根据因子得分系数矩阵求得每个古茶树单株资源的叶片表型性状在各主成分上的因子得分和综合得分,筛选出了8个叶片综合得分高的单株资源。根据19个形态性状指标按类间平均距离连接法进行聚类,在欧式距离为15.5处可把供试的74份古茶树资源聚为7个类群,聚类结果显示九万山古茶树资源存在丰富的遗传多样性和基因的相对稳定性,从形态学上揭示了各单株资源间的亲缘关系,可为该资源的保护和进一步的开发利用提供形态学上的参考依据。19 kinds of morphological characters of ancient tea resources including tree shape, tree performance,leaf, etc., were invested and analyzed statistically, which were located in the area of Mountain Jiuwan of Liuzhou in Guangxi. The statistical analysis of morphological diversity and correlation was carried out according to the values of these characters, and the dendrogram of cluster analysis was carried out also, in order to study the relationship between the resources of the 74 ancient tea trees and the variation law. The results showed that, the diversity index and variable coefficient of 13 qualitative characters were 0.57~1.59 and 23.11%~72.87% respectively. And their averages were 1.06 and 38.36% respectively. The diversity index and variable coefficient of 6 quantitative characters were 1.61~2.08 and 9.59%~29.21% respectively. And their averages were 1.95 and 15.74 respectively. 19 kinds of morphological characters of 74 ancient tea resources were studied by correlation analysis with Pearson correlation coefficient. Results showed that, 18 pairs of characters showed extremely significant positive correlation and 7 pairs of characters showed significant positive correlation. 5 pairs of characters showed extremely significant negative correlation and 6 pairs of characters showed significant negative correlation. The phenotypic characters of 17 leaves were analyzed by principal component analysis. And 6 principal components whose cumulative contribution rate reached to 75.868% were extracted, which contained most information of the original variables. Then some characters which had the most influence on leaf shape were selected and a few characters with little reference value were eliminated according to the correlation between the morphological characters and the principal components, which can be used as the main morphological characters in the variety breeding and classification study in the future. Based on the factor score coefficient matrix, the factor scores and comprehensive score of the leaf phenotyp
分 类 号:S571.1[农业科学—茶叶生产加工]
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