引用本文: | 程雪艳,滕爽爽,肖国强,邵艳卿,张炯明,方 军,柴雪良.基于微卫星的泥蚶5个地理群体遗传多样性分析[J].海洋科学,2015,39(11):116-125. |
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基于微卫星的泥蚶5个地理群体遗传多样性分析 |
程雪艳1, 滕爽爽2,3, 肖国强2,3, 邵艳卿2,3, 张炯明2,3, 方 军2,3, 柴雪良1
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1.温州医科大学 检验医学院 生命科学学院;2.浙江省海洋水产养殖研究所;3.浙江省近岸水域生物资源开发和保护重点实验室
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摘要: |
为了研究泥蚶不同地理群体的系统发育关系和遗传多样性, 运用微卫星DNA 标记, 对浙江温州(ZJ)、山东日照(SD)、韩国釜山(KR)、广西企沙(GX)、海南海口(HN)等5 个泥蚶地理群体进行了17 个基因位点的遗传多样性分析。结果显示, 从17 对引物中共检测出115 个等位基因, 每个位点等位基因数(Na) 2~12 个。有效等位基因数(Ne)为1.192~7.849, 平均观测杂合度(Ho)为0.430~0.516, 平均期望杂合度(He)为0.573~0.656, 5 个群体的平均多态信息含量(PIC)为0.525~0.608。Hardy-Weinberg 平衡检验表明, 51.2%的微卫星位点偏离平衡状态(P<0.05)。5 个群体间的种群遗传分化系数(FST)为0.012~0.062,呈现出较低的遗传分化。UPMGA 聚类分析表明, 浙江群体和韩国群体首先聚在一起, 亲缘关系最近,然后与海南群体聚合; 广西群体和山东群体亲缘关系较近, 聚为一支, 然后与上面三个群体聚合。 |
关键词: 泥蚶 微卫星 遗传多样性 地理群体 |
DOI:10.11759/hykx20150817001 |
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基金项目:国家高技术研究发展计划(2012AA10A410-1); 浙江省重大科技专项(2012C12907-4); 浙江省科技计划项目(2013F50016); 温州市种子种苗科技创新专项(N20120017) |
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Microsatellite analysis of genetic diversity in five geographic populations of Tegillarca granosa |
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Abstract: |
Genetic diversity was analyzed in five geographic populations of Tegillarca granosa by using 17 polymorphic microsatellite markers. The five different populations were sampled in Wenzhou City, Zhejiang Province; Rizhao City, Shandong Province; Qisha City, Guangxi Province; Haikou City, Hannan Province and Korea. In the seventeen microsatellite markers, 115 alleles were found and allele number per locus was 2-12. Mean effective allele number, mean observed and expected heterozygosity, and mean polymorphism information content were 1.192-7.849, 0.430-0.516 and 0.573-0.656 and 0.525-0.608, respectively. The Hardy-weinberg balance test showed that 51.2% microsatellite sites deviated from equilibrium(P< 0.05). The FST value of the five geographic populations ranged from 0.012 to 0.062, revealing a low level of genetic differentiation among them. The UPMGA tree indicated that Zhejiang and Korea populations clustered firstly, then clustered with Hainan population, and the Guangxi population clustered with Shandong population into another clade. |
Key words: Tegillarca granosa microsatellites genetic variability geographical populations |