甜菜品种（系）的ISSR标记数字指纹图谱构建及聚类分析

摘要：本文针对来源于荷兰的4个引进甜菜品种和国内的6个甜菜品系（其中2个为一年生野生甜菜）进行了ISSR指纹图谱构建和聚类分析研究。筛选出稳定性高且多态性好的6个引物用于试验。利用筛选的6条引物ISSR-PCR 共扩增出51个条带， 其中多态性条带百分率为86.3%. 利用该6条引物ISSR-PCR建立的指纹图谱能将试验中的全部甜菜品种都鉴定区分开。只利用2条引物L1和UBC846 扩增的8个多态性条带构建了10个甜菜品种（系）的数字指纹识别码，该数字指纹图谱能完全区分10个甜菜品种（系），结果显示ISSR 指纹图谱能非常有效的鉴定不同的甜菜品种。利用生物软件NTSYS-pc针对10个试验甜菜品种（系）的ISSR 扩增条带进行遗传相似性聚类分析，结果显示10个甜菜品种（系）的相似系数为0.43与0.83之间，平均为0.62。利用非加权组平均法（UPGMA）进行聚类分析，结果显示10个甜菜品种（系）聚类为2个组和3个亚组。UPGMA 聚类分析能清楚的显示10个甜菜群体间的遗传关系并且聚类结果与10个甜菜群体的特性一致， 说明ISSR标记能用于甜菜不同群体间遗传距离的评估。

Construction of digital fingerprinting and cluster analysis using ISSR markers for sugar beet cultivars (lines)

ISSR fingerprinting and cluster analysis were conducted among 4 introduced sugar beet (B. vulgaris) cultivars from Holland and 6 lines (including 2 annual beets) from China using ISSR markers. Six stable and high polymorphic primes were selected in this study. 51 bands were obtained using ISSR -PCR with the six primers, and polymorphic rate of the bands was 86.3 %. All test varieties (lines) can be distinguished by ISSR fingerprinting using the six primes. Digital fingerprint codes were constructed by 8 polymorphic bands from ISSR primer L1 and UBC846, which could discriminate 10 cultivars (lines) completely. The result indicated that ISSR fingerprinting could identify different sugar beet varieties efficiently. The genetic similarity matrix among all 10 samples was obtained by similarity analysis using Dice algorithm with NTSYS-pc software, and similarity coefficients ranged from 0.43 to 0.83 with an average of 0.62. Cluster tree was formed using UPGMA method. Cluster analysis showed that 10 varieties (lines) were divided two groups and three subgroups. UPGMA cluster analysis showed clear genetic relationships among the 10 sugar beet cultivars. The result indicated that the cluster tree was in accordance with the character of the populations and ISSR markers could be used to estimate genetic distance between populations.