High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

ABSTRACT. The genetic differentiation of grapevine rootstock varieties was inferred by the Artificial Neural Network approach based on the Self-Organizing Map algorithm. A combination of RAPD and SSR molecular markers, yielding polymorphic informative loci, was used to determine the genetic characterization among the rootstock varieties 420-A, Schwarzmann, IAC-766 Campinas, Traviú, Kober 5BB, and IAC-572 Jales. A neural network algorithm, based on allelic frequency, showed that the individual grapevine rootstocks (n = 64) were grouped into three genetically differentiated clusters. Cluster 1 included only the Kober 5BB rootstock, Cluster 2 included rootstocks of the varieties Traviú and IAC-572, and Cluster 3 included 420-A, Schwarzmann and IAC-766 plants. Evidence from the current study indicates that, despite the morphological similarities of the 420-A and Kober 5BB varieties, which share the same genetic origin, two new varieties were generated that are genetically divergent and show differences in performance.

摘要：本研究采用基于自组织映射（Self-Organizing Map）算法的人工神经网络（Artificial Neural Network）方法，对葡萄砧木品种的遗传分化进行推断。通过结合可产生多态性信息位点的随机扩增多态性DNA（RAPD）与简单序列重复（SSR）分子标记，对420-A、Schwarzmann、IAC-766 Campinas、Traviú、Kober 5BB及IAC-572 Jales共6个葡萄砧木品种的遗传特征进行解析。基于等位基因频率的神经网络算法显示，64份供试葡萄砧木材料可被划分为3个遗传分化显著的类群，其中类群1仅包含Kober 5BB砧木，类群2涵盖Traviú与IAC-572两个品种的砧木，类群3则包含420-A、Schwarzmann及IAC-766供试材料。本研究结果表明，尽管420-A与Kober 5BB品种形态相似且遗传起源一致，但二者已分化为两个遗传差异显著且表现性状存在差异的新品种。