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25个无花果品系间种质资源的RAPD分析 被引量:5

RAPD Analysis of Germplasm Resources in 25 Ficus carica Materials
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摘要 目的:采用RAPD技术对不同来源的25个无花果栽培品系进行种质资源亲缘关系分析,并从分子水平加以分类,为进一步构建无花果DNA指纹图谱和品种性状改良奠定基础。方法:以25种无花果叶片为材料提取其基因组DNA,采用40条随机引物对其进行RAPD扩增。利用DPS v7.5软件计算遗传距离,利用UPGMA方法进行聚类分析。结果:40条引物共扩增出257条带,其中139条为多态性条带,多态位点率为54.1%。遗传距离变化范围在0.025~0.874,平均为0.449。在遗传距离为0.83时,可将25个无花果品系分为2大类:布兰瑞克为一类;其他24个品系聚为一类。大多数品系均聚集在遗传距离小于0.17的范围内。同时利用RAPD分子标记鉴别出了7、18、23号无花果。结论:25个无花果品系间遗传多样性较低,种群间相关性较强。 Objective:To analyze the genetic relationship of germplasm resources in 25 Ficus carica from different regions by using RAPD technology and classify under the molecular level,in order to lay a foundation for further construction of Ficus carica DNA fingerprint and improvement of variety traits.Methods:The genomic DNA was extracted with the leaves of 25 species of Ficus carica as materials.RAPD-PCR was performed with 40 random primers,the genetic distance was calculated by DPS v7.5 software and cluster analysis was conducted by UPGMA method.Results:A total of 257 reproducible bands were amplified from 40 primers,including 139 polymorphic bands and the polynum was 54.1%.The variation range of genetic distance was 0.025~0.874 with an average of 0.449.When the genetic distance was 0.83,25 Ficus carica materials were divided into 2 groups:Bulanruike was one group,the other 24 species were another group.Most species were clustered with the genetic distance less than 0.17.The Ficus carica of 7,18,23 numbers were identified by RAPD molecular markers.Conclusion:The genetic diversity of 25 Ficus carica is low and the correlation between the populations is strong.
作者 陈桂平 鲁雪林 CHEN Gui-ping;LU Xue-lin(Department of Life Sciences of Tangshan Normal University, Tangshan 063000, China;Institute of Coastal Agriculture, Hebei Academy of Agriculture and Forestry Science,Tangshan 063299,China)
出处 《中药材》 CAS 北大核心 2019年第2期289-293,共5页 Journal of Chinese Medicinal Materials
基金 河北省重点研发计划自筹项目(17276340) 河北省财政专项(F18R18001-4)
关键词 无花果 RAPD 遗传多样性 聚类分析 Ficus carica Linn. RAPD Germplasm resources Cluster analysis
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