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柱花草种间亲缘关系SSR分析
引用本文:丁西朋, 罗小燕, 张龙, 王文强, 白昌军. 柱花草种间亲缘关系SSR分析[J]. 草业科学, 2015, 9(10): 1594-1602. DOI: 10.11829/j.issn.1001-0629.2015-0155
作者姓名:丁西朋  罗小燕  张龙  王文强  白昌军
作者单位:1.中国热带农业科学院热带作物品种资源研究所,海南 儋州 571737
摘    要:为研究柱花草(Stylosanthes spp.)种间的亲缘关系,本研究利用23个SSR标记对柱花草属14个种的33份材料的遗传关系进行了研究。结果表明,23个SSR标记在不同柱花草种间均具有多态性,共检测到等位基因138个,每个标记可检测到4~10个等位基因,平均6个;每个标记的多态性信息含量在0.501~0.830,平均为0.700。聚类分析和主成分分析结果表明, 33份供试柱花草种质可分为5类,根据包含的基因组类型分别命名为AB类、AC类、J类、DE类、G类。毛叶柱花草(S. subsericea)和有钩柱花草(S. hamata)(I)的亲缘关系最近,有钩柱花草(I)与头状柱花草(S. capitata)的遗传关系较远。

关 键 词:柱花草  SSR  亲缘关系  聚类分析  主成分分析
收稿时间:2015-03-19

Genetic relationships among Stylosanthes species revealed by SSR analysis
Xi-peng DING, Xiao-yan LUO, Long ZHANG, Wen-qiang WANG, Chang-jun BAI. Genetic relationships among Stylosanthes species revealed by SSR analysis[J]. Pratacultural Science, 2015, 9(10): 1594-1602. DOI: 10.11829/j.issn.1001-0629.2015-0155
Authors:Xi-peng DING  Xiao-yan LUO  Long ZHANG  Wen-qiang WANG  Chang-jun BAI
Affiliation:1.Institute of Tropical Crops Genetic Resources, CATAS, Danzhou 571737, China
Abstract:To investigate genetic relationships among Stylosanthes species, a total of 33 accessions from 14 Stylosanthes species were studied using 23 simple sequence rpeats (SSR) markers. All 23 SSR markers were polymorphic among 33 Stylosanthes accessions. A total of 138 alleles were generated by 23 SSR markers among different Stylosanthes accessions. The number of alleles per SSR marker ranged from 4 to 10 with an average of 6 alleles. The polymorphic information content per SSR marker varied from 0.501 to 0.830, with an average of 0.700. Based on the results of analysis of cluster and principal components, the 33 Stylosanthes accessions were divided into 5 separate clusters, which were named to be cluster AB, cluster AC, cluster J, cluster DE and cluster G based on genome contained. The highest and lowest genetic relations were presented between S. subsericea and S. hamata (I), S. hamata (I) and S. capitata, respectively.
Keywords:Stylosanthes  SSR markers  genetic relationship  cluster analysis  principal components analysis
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