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



Título del documento: High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks
Revista: Acta scientiarum. Agronomy
Base de datos: PERIÓDICA
Número de sistema: 000460000
ISSN: 1679-9275
Autors: 1
1
2
2
3
3
Institucions: 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
Any:
Volum: 42
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, analítico
Resumen en inglés 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
Paraules clau: 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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