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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
27/12/2012 |
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Data da última atualização: |
20/02/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
VIANA, J. M. S.; DELIMA, R. O.; FARIA, V. R.; MUNDIM, G. B.; RESENDE, M. D. V. de; SILVA, F. F. e. |
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Afiliação: |
JOSE MARCELO SORIANO VIANA, UFV; RODRIGO OLIVEIRA DELIMA, UFV; VINÍCIUS RIBEIRO FARIA, UFV; GABRIEL BORGES MUNDIM, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; FABIANO FONSECA E SILVA, UFV. |
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Título: |
Relevance of pedigree, historical data, dominance, and data unbalance for selection efficiency. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Agronomy Journal, v. 104, n. 3, p. 722-728, 2012. |
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Idioma: |
Inglês |
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Conteúdo: |
The objective of this study was to assess the impact of pedigree, historical data, dominance, and data unbalance on the estimation and precision of genetic variances and breeding values and on the selection efficiency in annual crop breeding. Expansion volume and grain yield from 12 trials of inbred progeny and four tests of non-inbred families were used in the analyses. The S1 to S5 progeny trials were designed as incomplete blocks, the S6 progeny trials were designed as complete blocks, and the half- and full-sib family trials were designed as lattices. The half-sib, full-sib, and inbred family models were fitted in across-generation analyses. One complete and four reduced models were used to assess the relevance of pedigree, historical data, and dominance. Simulated plot losses of 30% in the half- and full-sib progeny trials were used to study the influence of data unbalance. All analyses were performed using ASReml. Ignoring pedigree information or ancestor data and simulating plot losses determined relevant biases in estimating the additive and dominance variances, marked reduction in the precision of the predicted breeding values, significant changes in the classification of the breeding values, and errors in identifying superior individuals, i.e., a significant reduction in the selection efficiency. In contrast, excluding dominance had no significant effect on either the ranking of breeding values or selection efficiency. Our results revealed that best linear unbiased prediction including pedigree and historical data, based on a model with dominance, is the ideal method for genetic evaluation by plant breeders even when lost records are considered. MenosThe objective of this study was to assess the impact of pedigree, historical data, dominance, and data unbalance on the estimation and precision of genetic variances and breeding values and on the selection efficiency in annual crop breeding. Expansion volume and grain yield from 12 trials of inbred progeny and four tests of non-inbred families were used in the analyses. The S1 to S5 progeny trials were designed as incomplete blocks, the S6 progeny trials were designed as complete blocks, and the half- and full-sib family trials were designed as lattices. The half-sib, full-sib, and inbred family models were fitted in across-generation analyses. One complete and four reduced models were used to assess the relevance of pedigree, historical data, and dominance. Simulated plot losses of 30% in the half- and full-sib progeny trials were used to study the influence of data unbalance. All analyses were performed using ASReml. Ignoring pedigree information or ancestor data and simulating plot losses determined relevant biases in estimating the additive and dominance variances, marked reduction in the precision of the predicted breeding values, significant changes in the classification of the breeding values, and errors in identifying superior individuals, i.e., a significant reduction in the selection efficiency. In contrast, excluding dominance had no significant effect on either the ranking of breeding values or selection efficiency. Our results revealed that best linear unbiased... Mostrar Tudo |
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Palavras-Chave: |
Cultura anual; Valor genético. |
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Thesagro: |
Estimativa; Seleção Genética. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02358naa a2200229 a 4500
001 1943606
005 2015-02-20
008 2012 bl uuuu u00u1 u #d
100 1 $aVIANA, J. M. S.
245 $aRelevance of pedigree, historical data, dominance, and data unbalance for selection efficiency.$h[electronic resource]
260 $c2012
520 $aThe objective of this study was to assess the impact of pedigree, historical data, dominance, and data unbalance on the estimation and precision of genetic variances and breeding values and on the selection efficiency in annual crop breeding. Expansion volume and grain yield from 12 trials of inbred progeny and four tests of non-inbred families were used in the analyses. The S1 to S5 progeny trials were designed as incomplete blocks, the S6 progeny trials were designed as complete blocks, and the half- and full-sib family trials were designed as lattices. The half-sib, full-sib, and inbred family models were fitted in across-generation analyses. One complete and four reduced models were used to assess the relevance of pedigree, historical data, and dominance. Simulated plot losses of 30% in the half- and full-sib progeny trials were used to study the influence of data unbalance. All analyses were performed using ASReml. Ignoring pedigree information or ancestor data and simulating plot losses determined relevant biases in estimating the additive and dominance variances, marked reduction in the precision of the predicted breeding values, significant changes in the classification of the breeding values, and errors in identifying superior individuals, i.e., a significant reduction in the selection efficiency. In contrast, excluding dominance had no significant effect on either the ranking of breeding values or selection efficiency. Our results revealed that best linear unbiased prediction including pedigree and historical data, based on a model with dominance, is the ideal method for genetic evaluation by plant breeders even when lost records are considered.
650 $aEstimativa
650 $aSeleção Genética
653 $aCultura anual
653 $aValor genético
700 1 $aDELIMA, R. O.
700 1 $aFARIA, V. R.
700 1 $aMUNDIM, G. B.
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. F. e
773 $tAgronomy Journal$gv. 104, n. 3, p. 722-728, 2012.
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Embrapa Florestas (CNPF) |
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| Registros recuperados : 3 | |
| 1. |  | VOLL, E.; SILVA, E. A.; IMAI, N. N.; ANTUNIASSI, U. R.; VOLL, C. E. Controle localizado de plantas daninhas com GPS em área de produção de soja. In: REUNIÃO DE PESQUISA DE SOJA DA REGIÃO CENTRAL DO BRASIL, 27., 2005. Cornélio Procópio. Resumos... Londrina: Embrapa Soja, 2005. p. 511-512. (Embrapa Soja. Documentos, 257). Organizado por Odilon Ferreira Saraiva, Janete Lasso Ortiz, Simone Ery Grosskopf.| Biblioteca(s): Embrapa Soja. |
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| 2. | | SILVA, E. A.; VOLL, E.; IMAI, N. N.; ANTUNIASSI, U. R. Metodologia de suporte ao manejo localizado de plantas daninhas, em área de produção de soja, no Paraná. In: CONGRESSO BRASILEIRO DA CIÊNCIA DAS PLANTAS DANINHAS, 23., 2002, Gramado. Resumos... Londrina: SBCPD: Embrapa Clima Temperado, 2002. p.293.| Biblioteca(s): Embrapa Soja. |
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| 3. | | OSCO, L. P.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; MORIYA, E. A. S.; IMAI, N. N.; ESTRABIS, N.; IANCZYK, F.; ARAÚJO, F. F.; LIESENBERG, V.; JORGE, L. A. de C.; LI, J.; MA, L.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; CRESTE, J. E. A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements. Remote Sensing, n. 12, v. 6, a. 906, 2020. 1 - 21| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
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| Registros recuperados : 3 | |
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