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



Document title: High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks
Journal: Acta scientiarum. Agronomy
Database: PERIÓDICA
System number: 000460000
ISSN: 1679-9275
Authors: 1
1
2
2
3
3
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:
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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