Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/32729
metadata.artigo.dc.title: Inclusion of dominance effects in the multivariate GBLUP model
metadata.artigo.dc.creator: Santos, Jhonathan Pedroso Rigal dos
Vasconcellos, Renato Coelho de Castro
Pires, Luiz Paulo Miranda
Balestre, Marcio
Von Pinho, Renzo Garcia
metadata.artigo.dc.publisher: PLoS ONE
metadata.artigo.dc.date.issued: 2016
metadata.artigo.dc.identifier.citation: SANTOS, J. P. R. dos et al. Inclusion of dominance effects in the multivariate GBLUP model. PLoS ONE, [S. l.], v. 11, n. 4, 2016.
metadata.artigo.dc.description.abstract: New 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.
metadata.artigo.dc.identifier.uri: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152045
http://repositorio.ufla.br/jspui/handle/1/32729
metadata.artigo.dc.language: en_US
Appears in Collections:DAG - Artigos publicados em periódicos
DES - Artigos publicados em periódicos

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