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|metadata.artigo.dc.title:||Potential use of molecular markers for prediction of genotypic values in hybrid maize performance|
Von Pinho, R. G.
Souza, J. C.
Oliveira, R. L.
Maize - Hybrids
Best lenear unbiased predictions
Milho - Híbridos
Melhores previsões lineares imparciais
|metadata.artigo.dc.publisher:||Fundação de Pesquisas Científicas de Ribeirão Preto|
|metadata.artigo.dc.identifier.citation:||BALESTRE, M.; VON PINHO, R. G.; SOUZA, J. C.; OLIVEIRA, R. L. Potential use of molecular markers for prediction of genotypic values in hybrid maize performance. Genetics and Molecular Research, [S. l.], v. 8, n. 4, p. 1292-1306, 2009.|
|metadata.artigo.dc.description.abstract:||We evaluated the potential of genetic distances estimated by microsatellite markers for the prediction of the performance of single-cross maize hybrids. We also examined the potential of molecular markers for the prediction of genotypic values and the applicability of the Monte Carlo method for a correlation of genetic distances and grain yield. Ninety S0:2 progenies derived from three single-cross hybrids were analyzed. All 90 progenies were genotyped with 25 microsatellite markers, including nine markers linked to quantitative trait loci for grain yield. The genetic similarity datasets were used for constructing additive genetic and dominance matrices that were subsequently used to obtain the best linear unbiased prediction of specific combining ability and general combining ability. The genetic similarities were also correlated with grain yield, specific combining ability and heterosis of the hybrids. Genetic distances had moderate predictive ability for grain yield (0.546), specific combining ability (0.567) and heterosis (0.661). The Monte Carlo simulation was found to be a viable alternative for a correlation of genetic distances and grain yield. The accuracy of genotypic values using molecular data information was slightly higher than if no such information was incorporated. The estimation of the relationship using molecular markers proved to be a promising method for predicting genetic values using mixed linear models, especially when information about pedigree is unavailable.|
|Appears in Collections:||DBI - Artigos publicados em periódicos|
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