Genome prediction accuracy of common bean via Bayesian models.

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dc.contributorLEIRI DAIANE BARILI, UFV; NAINE MARTINS DO VALE, COODETEC; FABYANO FONSECA E SILVA, UFV; JOSÉ EUSTAQUIO DE SOUZA CARNEIRO, UFV; HINAYAH ROJAS DE OLIVEIRA, UFV; ROSANA PEREIRA VIANELLO, CNPAF; PAULA ARIELLE M RIBEIRO VALDISSER, CNPAF; MOYSES NASCIMENTO, UFV.
dc.creatorBARILI, L. D.
dc.creatorVALE, N. M. do
dc.creatorSILVA, F. R. e
dc.creatorCARNEIRO, J. E. de S.
dc.creatorOLIVEIRA, H. R. de
dc.creatorVIANELLO, R. P.
dc.creatorVALDISSER, P. A. M. R.
dc.creatorNASCIMENTO, M.
dc.date2018-09-18T00:41:16Z
dc.date2018-09-18T00:41:16Z
dc.date2018-09-17
dc.date2018
dc.date2018-09-18T00:41:16Z
dc.date.accessioned2026-07-01T00:04:12Z
dc.descriptionWe aimed to apply genomic information based on SNP (single nucleotide polymorphism) markers for the genetic evaluation of the traits ?stay-green? (SG), plant architecture (PA), grain aspect (GA) and grain yield (GY) in common bean through Bayesian models. These models were compared in terms of prediction accuracy and ability for heritability estimation for each one of the mentioned traits. A total of 80 cultivars were genotyped for 377 SNP markers, whose effects were estimated by five different Bayesian models: Bayes A (BA), B (BB), C (BC), LASSO (BL) e Ridge regression (BRR). Although, prediction accuracies calculated by means of cross-validation have been similar within each trait, the BB model stood out for the trait SG, whereas the BRR was indicated for the remaining traits. The heritability estimates for the traits SG, PA, GA and GY were 0.61, 0.28, 0.32 and 0.29, respectively. In summary, the Bayesian methods applied here were effective and ease to be implemented. The used SNP markers can help in the early selection of promising genotypes, since incorporating genomic information increase the prediction accuracy of the estimated genetic merit.
dc.identifierCiência Rural, v. 48, n. 8, e20170497, 2018.
dc.identifier1678-4596
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1095835
dc.identifier10.1590/0103-8478cr20170497
dc.identifier.urihttp://hdl.handle.net/123456789/396701
dc.languageeng
dc.rightsopenAccess
dc.subjectValidação cruzada
dc.subjectCross-validation
dc.subjectFeijão
dc.subjectPhaseolus Vulgaris
dc.subjectMarcador Molecular
dc.subjectBeans
dc.subjectGenetic markers
dc.subjectMarker-assisted selection
dc.titleGenome prediction accuracy of common bean via Bayesian models.
dc.typeArtigo de periódico

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