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- Title
High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks.
- Authors
Costa, Marcia Oliveira; Capel, Livia Santos; Maldonado, Carlos; Mora, Freddy; Mangolin, Claudete Aparecida; de Fátima Pires da Silva Machado, Maria
- 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.
- Subjects
ROOTSTOCKS; ARTIFICIAL neural networks; GRAPES; SELF-organizing maps
- Publication
Acta Scientiarum: Agronomy, 2020, Vol 42, p1
- ISSN
1679-9275
- Publication type
Article
- DOI
10.4025/actasciagron.v42i1.43475