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

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ACTA SCIENTIARUM-AGRONOMY
卷 42, 期 -, 页码 -

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UNIV ESTADUAL MARINGA, PRO-REITORIA PESQUISA POS-GRADUACAO
DOI: 10.4025/actasciagron.v42i1.43475

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clustering methods; RAPD-SSR loci; self-organizing map algorithm

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  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Capes)

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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, Traviu, 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 Traviu 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.

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