关键词: 烟草, ISSR, 遗传多样性, 聚类分析

Abstract: 【OBJECTIVE】Assessing the genetic diversity among tobacco accessions and analyzing the genetic relationships between various types of tobacco provide the essential information for the mining and utilization of tobacco germplasms;【METHOD】One hundred and nineteen tobacco accessions including various types of tobacco were used for ISSR analysis. The genetic similarity coefficients were estimated and cluster analysis was carried out using UPGMA method;【RESULTS】Six hundred and seventy-two bands, all of which were polymorphic, were amplified using 21 ISSR primers. Out of them, one hundred and sixteen bands were specific for N. tabacum. Pairwise genetic similarity coefficients among accessions in N. tabacum ranged from 0.779 to 0.945, with an average of 0.868. Among the flue-cured tobacco varieties, genetic similarity coefficients ranged from 0.812 to 0.933, with an average of 0.876. The tobacco accessions could be clustered into the different subgroups or classes according to the types of tobacco. The flue-cured tobacco accessions introduced from abroad were obviously not separated from the domestic accessions including landraces and varieties developed in China. There are low genetic similarity coefficients between the accessions in N. tabacum and other tobacco species. The species N. tabacum and its progenitor N. sylvestris were clustered into the same class, and N. longiflora and N. plumbaginifolia, both of which belong to the group Alatae of the subgenera Petunioides, were clustered into the same class. The results obtained in the ISSR dendrogram agreed with the presumed genetic differentiation;【CONCLUSION】There was low genetic diversity among the flue-cured tobacco germplasms in China. The germplasms clustered into the different subgroups or classes should be utilized in the selection of the parents. In order to widen the genetic base of the flue-cured tobacco varieties, the genetic potentials in the wild tobacco should be explored.

Key words: Tobacco, ISSR, Genetic diversity, Cluster analysis