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| Registros recuperados : 89 | |
| 11. |  | SUSSEL, A. A. B.; SILVA NETO, S. P. da; MOREIRA, C. T.; ALVES, R. S.; ALMEIDA, N. J. Correlação de diferentes metodologias de avaliação da ferrugem asiática em linhagens de soja. In: CONGRESSO BRASILEIRO DE SOJA, 6., 2012, Cuiabá. Soja: integração nacional e desenvolvimento sustentável: anais. Brasília, DF: Embrapa; Londrina: Embrapa Soja, 2012. 1 CD-ROM. Editado por Adilson de Oliveira Júnior, Clara Beatriz Hoffmann-Campo, Regina Maria Villas Bôas de Campos Leite, Ricardo Vilela Abdelnoor. VI CBSoja.| Biblioteca(s): Embrapa Cerrados. |
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| 12. | | SUSSEL, A. A. B.; SILVA NETO, S. P. da; MOREIRA, C. T.; ALVES, R. S.; ALMEIDA, N. J. Diferentes parâmetros patométricos para avaliação da ferrugem asiática em linhagens transgênicas de soja. In: CONGRESSO BRASILEIRO DE SOJA, 6., 2012, Cuiabá. Soja: integração nacional e desenvolvimento sustentável: anais. Brasília, DF: Embrapa; Londrina: Embrapa Soja, 2012. 1 CD-ROM. Editado por Adilson de Oliveira Júnior, Clara Beatriz Hoffmann-Campo, Regina Maria Villas Bôas de Campos Leite, Ricardo Vilela Abdelnoor. VI CBSoja.| Biblioteca(s): Embrapa Cerrados. |
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| 14. | | TEODORO, P. E.; AZEVEDO, C. F.; FARIAS, F. J. C.; ALVES, R. S.; PEIXOTO, L. de A.; RIBEIRO, L. P.; CARVALHO, L. P. de; BHERING, L. L. Adaptability of cotton (Gossypium hirsutum) genotypes analysed using a Bayesian AMMI model. Crop and Pasture Science, v. 70, n. 7, p. 615-621, 2019.| Biblioteca(s): Embrapa Algodão. |
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| 15. | | CAIXETA, E. T.; RESENDE, M. D. V. de; ALKIMIM, E. R.; SOUSA, T. V.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; ALVES, R. S. Aceleração do melhoramento do cafeeiro via seleção genômica: agilidade e eficácia no lançamento de novas cultivares. Brasília, DF: Embrapa Café, 2022. 54 p. PDF. (Embrapa Café. Documentos, 17). Projeto SI04 - Genotipagem e seleção genômica em populações de melhoramento de Coffea arabica.| Biblioteca(s): Embrapa Café. |
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| 16. | | PEIXOTO, M. A.; EVANGELISTA, J. S. P. C.; ALVES, R. S.; FARIAS, F. J. C.; CARVALHO, L. P.; TEODORO, L. P. R.; TEODORO, P. E.; BHERING, L. L. Models for optimizing selection based on adaptability and stability of cotton genotypes. Ciência Rural, v. 51, n. 5, e20200530, p. 1-8, 2021. 8 p.| Biblioteca(s): Embrapa Algodão. |
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| 17. | | 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.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | ALMEIDA, A. S.; ALVES, R. S.; FILGUEIRAS, H. A. C.; MENEZES, J. B.; PEREIRA, M. E. C.; MOURA, C. F. H. Conservação de melão Cantaloupe Hy-Mark tratado em pós-colheita com 1- metilciclopropeno (1- MC.POSTAL). Horticultura Brasileira, Brasília, v. 21, n. 2, jul. 2003. Suplemento 2. Trabalho apresentado no 43º Congresso Brasileiro de Olericultura, 2003. Publicado também como resumo em: Horticultura Brasileira, Brasília, v. 21, n. 2, p. 382, jul. 2003. Suplemento 1.| Biblioteca(s): Embrapa Hortaliças. |
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| Registros recuperados : 89 | |
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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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Circulação/Nível: |
A - 1 |
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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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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.
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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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