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dc.creatorSantos, Jhonathan Pedroso Rigal dos-
dc.creatorVasconcellos, Renato Coelho de Castro-
dc.creatorPires, Luiz Paulo Miranda-
dc.creatorBalestre, Marcio-
dc.creatorVon Pinho, Renzo Garcia-
dc.date.accessioned2019-02-05T11:04:05Z-
dc.date.available2019-02-05T11:04:05Z-
dc.date.issued2016-
dc.identifier.citationSANTOS, J. P. R. dos et al. Inclusion of dominance effects in the multivariate GBLUP model. PLoS ONE, [S. l.], v. 11, n. 4, 2016.pt_BR
dc.identifier.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152045pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/32729-
dc.description.abstractNew proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.pt_BR
dc.languageen_USpt_BR
dc.publisherPLoS ONEpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourcehttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152045pt_BR
dc.titleInclusion of dominance effects in the multivariate GBLUP modelpt_BR
dc.typeArtigopt_BR
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