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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
10/06/2015 |
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Data da última atualização: |
10/06/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MUÑOZ, P. R.; RESENDE JUNIOR, M. F. R.; GEZAN, S. A.; RESENDE, M. D. V. de; CAMPOS, G. de los; KIRST, M.; HUBER, D.; PETER, G. F. |
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Afiliação: |
Patricio R. Muñoz, University of Florida; Marcio F. R. Resende Jr.; Salvador A. Gezan; MARCOS DEON VILELA DE RESENDE, CNPF; Gustavo de los Campos; Matias Kirst; Dudley Huber; Gary F. Peter, University of Florida. |
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Título: |
Unraveling additive from nonadditive effects using genomic relationship matrices. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Genetics, v. 198, p. 1759-1768, Dec. 2014. |
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DOI: |
10.1534/genetics.114.171322 |
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Idioma: |
Inglês |
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Conteúdo: |
The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies. MenosThe application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves ... Mostrar Tudo |
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Palavras-Chave: |
G-BLUP; Genomic selection; Matriz de relacionamento; Melhoramento genético; Relationship matrices; Seleção genômica. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02502naa a2200289 a 4500
001 2017257
005 2015-06-10
008 2014 bl uuuu u00u1 u #d
024 7 $a10.1534/genetics.114.171322$2DOI
100 1 $aMUÑOZ, P. R.
245 $aUnraveling additive from nonadditive effects using genomic relationship matrices.$h[electronic resource]
260 $c2014
520 $aThe application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.
653 $aG-BLUP
653 $aGenomic selection
653 $aMatriz de relacionamento
653 $aMelhoramento genético
653 $aRelationship matrices
653 $aSeleção genômica
700 1 $aRESENDE JUNIOR, M. F. R.
700 1 $aGEZAN, S. A.
700 1 $aRESENDE, M. D. V. de
700 1 $aCAMPOS, G. de los
700 1 $aKIRST, M.
700 1 $aHUBER, D.
700 1 $aPETER, G. F.
773 $tGenetics$gv. 198, p. 1759-1768, Dec. 2014.
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