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

Marcia   Oliveira Costa; Livia   Santos Capel; Carlos  Maldonado; Freddy  Mora; Claudete   Aparecida Mangolin; Maria de Fátima   Pires da Silva Machado

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

Autores

Marcia Oliveira Costa

Livia Santos Capel

Carlos Maldonado

Freddy Mora

Claudete Aparecida Mangolin

Maria de Fátima Pires da Silva Machado

Editor

Universidade Estadual de Maringá

Descripción

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.