| Journal: | Acta scientiarum. Agronomy |
| Database: | PERIÓDICA |
| System number: | 000460000 |
| ISSN: | 1679-9275 |
| Authors: | Costa, Marcia Oliveira1 Capel, Livia Santos1 Maldonado, Carlos2 Mora, Freddy2 Mangolin, Claudete Aparecida3 Machado, Maria de Fátima Pires da Silva3 |
| Institutions: | 1Universidade Estadual de Maringa, Maringa, Parana. Brasil 2Universidad de Talca, Instituto de Ciencias Biológicas, Talca. Chile 3Universidade Estadual de Maringa, Departmento de Biotecnologia, Genrtica e Biologia Celular, Maringa, Parana. Brasil |
| Year: | 2020 |
| Volumen: | 42 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Experimental, analítico |
| English 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 |
| Disciplines: | Agrociencias, Biología, Ciencias de la computación |
| Keyword: | Plantas para uso industrial, Genética, Redes, Modelos de cúmulos, Redes neuronales artificiales, Algoritmos, Vid, Portainjertos |
| Keyword: | Plants for industrial use, Genetics, Networks, Cluster method, RAPD, Algorithms, Artificial neural networks, Grapevine, Rootstocks |
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