Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110406
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dc.contributor.authorLIMA L. P.
dc.contributor.authorAZEVEDO, C. F.
dc.contributor.authorRESENDE, M. D. V. de
dc.contributor.authorSILVA, F. F. e
dc.contributor.authorSUELA, M. M.
dc.contributor.authorNASCIMENTO, M.
dc.contributor.authorVIANA, J. M. S.
dc.date.accessioned2020-06-10T04:06:30Z-
dc.date.available2020-06-10T04:06:30Z-
dc.date.created2019-07-05
dc.date.issued2019
dc.identifier.citationScientia Agricicola, v. 76, n. 4, p. 290-298, July/Aug. 2019.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1110406-
dc.descriptionGenome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.
dc.language.isoeng
dc.rightsopenAccesseng
dc.subjectGenomic prediction
dc.subjectGenetic gain
dc.subjectAsian rice
dc.titleNew insights into genomic selection through population-based non-parametric prediction methods.
dc.typeArtigo de periódico
dc.subject.thesagroOryza Sativa
dc.subject.thesagroArroz
dc.subject.nalthesaurusSelection index
riaa.ainfo.id1110406
riaa.ainfo.lastupdate2020-06-09
dc.identifier.doi10.1590/1678-992x-2017-0351
dc.contributor.institutionLeísa Pires Lima, Universidade Federal de Viçosa; Camila Ferreira Azevedo, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Matheus Massariol Suela, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; José Marcelo Soriano Viana, Universidade Federal de Viçosa.
Appears in Collections:Artigo em periódico indexado (CNPF)

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