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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
05/07/2019 |
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Data da última atualização: |
30/10/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. |
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Afiliação: |
Leonardo 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. |
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Título: |
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p. |
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DOI: |
10.1371/journal.pone.0215315 |
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Idioma: |
Inglês |
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Conteúdo: |
At 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. MenosAt 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... Mostrar Tudo |
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Palavras-Chave: |
Bayesian-inference; Breeding values; Genomic selection; Inferência Bayesian; Mixed models; Modelo misto; Seed protein; Seleção genômica. |
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Thesagro: |
Soja. |
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Thesaurus Nal: |
Agronomic traits; Prediction; Soybeans. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199239/1/2019-M.Deon-PO-Multi-trait.pdf
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Marc: |
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001 2110400
005 2019-10-30
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1371/journal.pone.0215315$2DOI
100 1 $aVOLPATO, L.
245 $aMulti-trait multi-environment models in the genetic selection of segregating soybean progeny.$h[electronic resource]
260 $c2019
520 $aAt 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.
650 $aAgronomic traits
650 $aPrediction
650 $aSoybeans
650 $aSoja
653 $aBayesian-inference
653 $aBreeding values
653 $aGenomic selection
653 $aInferência Bayesian
653 $aMixed models
653 $aModelo misto
653 $aSeed protein
653 $aSeleção genômica
700 1 $aALVES, R. S.
700 1 $aTEODORO, P. E.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aLUDKE, W. H.
700 1 $aSILVA, F. L. da
700 1 $aBORÉM, A.
773 $tPLoS ONE$gv. 14, n. 4, e0215315, Apr. 2019. 22 p.
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Registro original: |
Embrapa Florestas (CNPF) |
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Biblioteca |
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| Registros recuperados : 13 | |
| 4. |  | SANCHÉZ, C. F. B.; ALVES, R. S.; GARCIA, A. d. P.; TEODORO, P. E.; PEIXOTO, L. A.; SILVA, L. A.; BHERING, L. L.; RESENDE, M. D. V. de. Estimates of repeatability coefficients and the number of the optimum measure to select superior genotypes in Annona muricata L. Genetics and Molecular Research, v. 16, n. 3, gmr16039753, 2017. 8 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas. |
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| 5. | | VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 6. | | ALVES, R. S.; PEIXOTO, L. de A.; TEODORO, P. E.; SILVA, L. A.; RODRIGUES, E. V.; RESENDE, M. D. V. de; LAVIOLA, B. G.; BHERING, L. L. Selection of Jatropha curcas families based on temporal stability and adaptability of genetic values. Industrial Crops and Products, v. 119, p. 290-293, Sept. 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agroenergia; Embrapa Florestas. |
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| 7. | | SOUZA, N. O. de; ALVES, R. S.; TEODORO, P. E.; SILVA, L. A. da; TARDIN, F. D.; BALDONI, A. B.; RESENDE, M. D. V. de; BHERING, L. L. Single- and multiple-trait BLUP in genetic selection of parents and hybrids of grain sorghum. Revista de la Facultad de Ciencias Agrarias Uncuyo, v. 51, n. 2, Jul./Dic. 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Agrossilvipastoril; Embrapa Florestas; Embrapa Milho e Sorgo. |
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| 8. | | ESCOBAR, J. A. D.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F.; BARBOSA, M. H. P.; NUNES, A. C. P.; ALVES, R. S.; NASCIMENTO, M. Teoria de valores extremos e tamanho amostral para o melhoramento genético do quantil máximo em plantas. Revista Brasileira de Biometria, Lavras, v. 36, n. 1, p. 108-127, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 3 |
| Biblioteca(s): Embrapa Florestas. |
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| 9. | | FERREIRA, F. M.; ROCHA, J. R. do A. S. de C.; ALVES, R. S.; ELIZEU, A. M.; BENITES, F. R. G.; RESENDE, M. D. V. de; SOUZA SOBRINHO, F. de; BHERING, L. L. Estimates of repeatability coefficients and optimum number of measures for genetic selection of Cynodon spp. Euphytica, v. 216, article 70, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas; Embrapa Gado de Leite. |
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| 10. | | ALVES, R. S.; TEODORO, P. E.; FARIAS, F. C.; FARIAS, F. J. C.; CARVALHO, L. P. de; RODRIGUES, J. I. S.; BHERING, L. L.; RESENDE, M. D. V. de. Evaluation of genotype x environment interactions in cotton using the method proposed by Eberhart and Russell and reaction norm models. Genetics and Molecular Research, v. 16, n. 3, gmr16039726, 2017.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Algodão; Embrapa Florestas. |
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| 11. | | ALVES, R. S.; TEODORO, P. E.; PEIXOTO, L. de A.; ROCHA, J. R. do A. S. de C.; SILVA, L. A.; LAVIOLA, B. G.; RESENDE, M. D. V. de; BHERING, L. L. Multiple-trait BLUP in longitudinal data analysis on Jatropha curcas breeding for bioenergy. Industrial Crops & Products, v.130, p. 558-561, 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agroenergia; Embrapa Florestas. |
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| 12. | | ALVES, R. S.; ROCHA, J. R. do A. de C.; TEODORO, P. E.; RESENDE, M. D. V. de; HENRIQUES, E. P.; SILVA, L. A.; CARNEIRO, P. C. S.; BHERING, L. L. Multiple-trait BLUP: a suitable strategy for genetic selection of Eucalyptus. Tree Genetics & Genomes, v. 14, n. 5, article 77, Oct. 2018. 8 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 13. | | VOLPATO, L.; SIMOQUELI, G. F.; ALVES, R. S.; ROCHA, J. R. do A. S. de C.; DEL CONTE, M. V.; RESENDE, M. D. V. de; CARNEIRO, P. C. S.; SILVA, F. L. da. Selection of inbred soybean progeny (Glycine max): an approach with population effect. Plant Breeding, v. 137, n. 6, p. 865-872, Dec. 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 13 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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