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dc.contributor.authorVOLPATO, L.eng
dc.contributor.authorALVES, R. S.eng
dc.contributor.authorTEODORO, P. E.eng
dc.contributor.authorRESENDE, M. D. V. deeng
dc.contributor.authorNASCIMENTO, M.eng
dc.contributor.authorNASCIMENTO, A. C. C.eng
dc.contributor.authorLUDKE, W. H.eng
dc.contributor.authorSILVA, F. L. daeng
dc.contributor.authorBORÉM, A.eng
dc.date.accessioned2019-07-06T01:05:45Z-
dc.date.available2019-07-06T01:05:45Z-
dc.date.created2019-07-05
dc.date.issued2019
dc.identifier.citationPLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1110400-
dc.descriptionAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectBayesian-inferenceeng
dc.subjectGenomic selectioneng
dc.subjectBreeding valueseng
dc.subjectSeed proteineng
dc.subjectMixed modelseng
dc.subjectInferência Bayesianeng
dc.subjectModelo mistoeng
dc.subjectSeleção genômicaeng
dc.titleMulti-trait multi-environment models in the genetic selection of segregating soybean progeny.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-10-30T11:11:11Z
dc.subject.thesagroSojaeng
dc.subject.nalthesaurusSoybeanseng
dc.subject.nalthesaurusAgronomic traitseng
dc.subject.nalthesaurusPredictioneng
riaa.ainfo.id1110400eng
riaa.ainfo.lastupdate2019-10-30 -03:00:00
dc.identifier.doi10.1371/journal.pone.0215315eng
dc.contributor.institutionLeonardo Volpato, Universidade Federal de Viçosa; Rodrigo Silva Alves, Universidade Federal de Viçosa; Paulo Eduardo Teodoro, Universidade Federal de Mato Grosso do Sul; MARCOS DEON VILELA DE RESENDE, CNPF; Moysés Nascimento, Universidade Federal de Viçosa; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Willian Hytalo Ludke, Universidade Federal de Viçosa; Felipe Lopes da Silva, Universidade Federal de Viçosa; Aluízio Borém, Universidade Federal de Viçosa.eng
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