10.6084/m9.figshare.10026332.v1 Marcia Oliveira Costa Marcia Oliveira Costa Livia Santos Capel Livia Santos Capel Carlos Maldonado Carlos Maldonado Freddy Mora Freddy Mora Claudete Aparecida Mangolin Claudete Aparecida Mangolin Maria de Fátima Pires da Silva Machado Maria de Fátima Pires da Silva Machado High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks SciELO journals 2019 clustering methods RAPD-SSR loci self-organizing map algorithm 2019-10-23 05:22:03 Dataset https://scielo.figshare.com/articles/dataset/High_genetic_differentiation_of_grapevine_rootstock_varieties_determined_by_molecular_markers_and_artificial_neural_networks/10026332 <div><p>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.</p></div>