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中国南瓜种质资源农艺性状与RAPD标记分析
引用本文:褚盼盼,向长萍,张称心,刘成平.中国南瓜种质资源农艺性状与RAPD标记分析[J].核农学报,2007,21(5):441-446.
作者姓名:褚盼盼  向长萍  张称心  刘成平
作者单位:华中农业大学园艺林学学院/园艺植物生物学教育部重点实验室,国家蔬菜改良中心华中分中心,湖北,武汉,430070
摘    要:本研究利用形态学标记和RAPD分子标记同时对70份来自我国不同地区的中国南瓜种质进行遗传多样性和亲缘关系分析。在所观察的56个有差异的农艺性状中,变异系数从6.60%~262.22%,平均变异系数为37.50%。从150个随机引物中筛选出21个进行RAPD分析,结果表明,在检测到的167条带中有130条具有多态性,多态带比率为73.23%;平均每个引物检测到的条带数多达8条,平均Shannon信息指数(Ⅰ)为0.268。基于农艺性状的聚类分析将70份中国南瓜种质分为七类。基于RAPD标记的聚类分析将70份中国南瓜种质分为五类,其聚类结果与形态特征有一定的相关性。但两种聚类结果均无法从地理来源上进行区分。

关 键 词:中国南瓜    遗传多样性    农艺性状    RAPD    聚类分析
文章编号:1000-8551(2007)05-441-06
收稿时间:2009-12-31
修稿时间:2007-01-09

GENETIC DIVERSITY OF Cucurbita moschata GENOTYPES REVEALED BY RAPD MARKERS AND AGRONOMIC TRAITS
CHU Pan-pan,XIANG Chang-ping,ZHANG Chen-xin,LIU Cheng-ping.GENETIC DIVERSITY OF Cucurbita moschata GENOTYPES REVEALED BY RAPD MARKERS AND AGRONOMIC TRAITS[J].Acta Agriculturae Nucleatae Sinica,2007,21(5):441-446.
Authors:CHU Pan-pan  XIANG Chang-ping  ZHANG Chen-xin  LIU Cheng-ping
Institution:College of Horticulture and Forestry, Huazhong Agricultural University/Key Laboratory of Horticulture Plant Biology Ministry of Education, Huazhong Branch of National Vegetable Improvement Center, Wuhan, Hubei 430070
Abstract:In this study,70 genotypes of Cucurbita moschata collected from different agro-ecological zones of China were analyzed for genetic diversity,based on the morphological and RAPD markers.Coefficient of variation(CV) based on agronomic traits ranged from 6.60% to 262.22% with the average of 37.50%.21 primers were screened from 150 arbitrary primers,and a total of 167 DNA bands were amplified,130(73.23%)of which were polymorphic.The number of bands per primer was 8,and the average of Shannon's information index was 0.268.The cluster analysis divided the germplasm into 7 groups based on agronomic traits,and 5 groups based on RAPD markers respectively.The RAPD clusters were greatly associated with the morphologic characters but couldn't divided the germplasm by origins.
Keywords:Cucurbita moschata  genetic diversity  agronomic traits  RAPD  cluster analysis
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