利用设计合成的60对扩增稳定的EST-SSR核心引物分析51份广西茶树地方品种的遗传多样性和遗传结构,共检测到232个等位基因,每对引物检测到2~7个,平均3.88个。群体的观测杂合度(H</em>o)变化范围为0.02(TM149) ~0.92(TM186),平均为0.37; 期望杂合度(H</em>e)的变化范围为0.13(TM144, TM147) ~0.73(TM189),平均为0.47; 位点的多态信息含量(PIC)介于0.12~0.68之间,平均为0.41。地区间遗传相似系数在0.83~0.94之间,说明不同地区地方品种间的亲缘关系较近。对于茶与白毛茶群体而言,平均遗传分化系数为0.02 (P<0.001),即有2%的遗传变异存在于群体间,而有98%的遗传变异存在于群体内。另外,种群内的近交系数F平均为0.21,说明群体内近交现象较严重。基于Nei’s遗传距离,利用PowerMarker 3.25 软件进行Neighbor-Joining聚类分析,将51份地方品种聚为3类: 桂林、来宾和贺州的大部分品种,钦州和梧州的全部品种聚在第Ⅰ类; 第Ⅱ类包含多数地区的品种; 第Ⅲ类主要由崇左、防城港和南宁的品种组成。该结果与基于模型的遗传结构分析结果基本一致。is
Sixty core EST-SSR primers, among which 59 were newly developed from the ESTs of tea plant, were selected and used to analyze genetic diversity and genetic structure of 51 landraces of tea (Camellia sinensis and C. sinensis var. pubilimba) distributed in Guangxi, China. A dendrogram based on genetic distance was constructed from software PowerMarker 3.25 using Neighbor-Joining clustering method. Two hundred and thirty-two alleles were amplified, and the number of alleles per primer ranged from 2 to 7, with an average of 3.88. The observed heterozygosity (H</em>o) ranged from 0.02 to 0.92 but expected heterozygosity (H</em>e) varied from 0.13 to 0.73, with average of 0.37 and 0.47, respectively. The average polymorphism information content (PIC) was 0.41. The genetic similarity coefficient among eight different regions was from 0.83 to 0.94, indicating there were close relationships among tea landraces in the different regions. Most of the diversity (98%) was found within populations, only 2% was between populations of C. sinensis and C. sinensis var. pubilimba. It was suggested that serious inbreeding occurred within the groups as shown by the coefficient of inbreeding (F=0.21). The 51 landraces from the different regions were grouped into three. Most tea landraces from Guilin, Laibin and Hezhou, and all from Qinzhou and Wuzhou were clustered into group Ⅰ of the dendrogram, while those from Chongzuo, Fangchenggang and Nanning were assigned into group Ⅲ. Other landraces were assigned into group Ⅱ. The results from Neighbor-Joining clustering method agreed with that from the model-based analysis of genetic structure.is
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