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| Registros recuperados : 12 | |
| 1. |  | SOUZA, B. M. de; FREITAS, M. L. M.; GEZAN, S. A.; ZANATTO, B.; AGUIAR, A. V. de. Genetic parameters, genotype-by-environment interaction, and genetic gains in Corymbia citriodora hook. In: SIMPOSIO INTERNACIONAL DE RECURSOS GENÉTICOS PARA LAS AMÉRICAS Y EL CARIBE, 11., 2017, Guadalajara. Resúmenes... Guadalajara: [s. n.], 2017. p. 172.| Biblioteca(s): Embrapa Florestas. |
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| 3. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARAES, C. T.; NODA, R. W.; SOUZA, J. C. de; PASTINA, M. M.; GUIMARAES, L. J. M. Seleção genômica para tolerância ao déficit hídrico em milho. In: CONGRESSO NACIONAL DE MILHO E SORGO, 31., 2016, Bento Gonçalves. Milho e sorgo: inovações, mercados e segurança alimentar: anais. Sete Lagoas: Associação Brasileira de Milho e Sorgo, 2016.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | 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. Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics, v. 198, p. 1759-1768, Dec. 2014.| Biblioteca(s): Embrapa Florestas. |
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| 5. | | SOUZA, B. M. de; FREITAS, M. L. M.; SEBBENN, A. M.; GEZAN, S. A.; ZANATTO, B.; ZULIAN, D. F.; LOPES, M. T. G.; LONGUI, E. L.; GUERRINI, I. A.; AGUIAR, A. V. de. Genotype-by-environment interaction in Corymbia citriodora (Hook.) K.D. Hill, & L.A.S. Johnson progeny test in Luiz Antonio, Brazil. Forest Ecology and Management, v. 460, article 117855, Mar. 2020. 8 p.| Biblioteca(s): Embrapa Florestas. |
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| 6. | | RIOS, E. F.; ANDRADE, M. H. M. L.; RESENDE JR, M. F. R.; KIRST, M.; RESENDE, M. D. V. de; ALMEIDA FILHO, J. O. E. de; GEZAN, S. A.; MUNOZ, P. Genomic prediction in family bulks using different traits and cross-validations in pine. G3: Genes, Genomes, Genetics, v. 11, n. 9, p. 1-12, 2021.| Biblioteca(s): Embrapa Café. |
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| 7. | | MULLER, B. S. F.; ALMEIDA FILHO, J. E. de; LIMA, B. M.; GARCIA, C. C.; MISSIAGGIA, A.; AGUIAR, A. M.; TAKAHASHI, E.; KIRST, M.; GEZAN, S. A.; SILVA JUNIOR, O. B. da; NEVES, L. G.; GRATTAPAGLIA, D. Independent and Joint-GWAS for growth traits in Eucalyptus by assembling genome-wide data for 3373 individuals across four breeding populations. The New phytologist, v. 221, n. 2, p. 818-833, 2019. Na publicação: Orzenil B. Silva-Junior.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 8. | | FERREIRA, F. M.; CHAVES, S. F. S.; BHERING, L. L.; ALVES, R. S.; TAKAHASHI, E. K.; SOUSA, J. E.; RESENDE, M. D. V. de; LEITE, F. P.; GEZAN, S. A.; VIANA, J. M. S.; FERNANDES, S. B.; DIAS, K. O. G. A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. Forest Ecology and Management, v. 548, Article 121393, 2023. 10 p.| Biblioteca(s): Embrapa Café. |
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| 9. | | OLIVEIRA, I. C. M.; GUILHEN, J. H. S.; RIBEIRO, P. C. de O.; GEZAN, S. A.; SCHAFFERT, R. E.; SIMEONE, M. L. F.; DAMASCENO, C. M. B.; CARNEIRO, J. E. de S.; CARNEIRO, P. C. S.; PARRELLA, R. A. da C.; PASTINA, M. M. Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. Field Crops Research, v. 257, 107929, 2020.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 10. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. Heredity, London, v. 121, n. 1, p. 24-37, 2018.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 11. | | PADUA, J. M. V.; DIAS, K. O. das G.; PASTINA, M. M.; SOUZA, J. C. de; QUEIROZ, V. A. V.; COSTA, R. V. da; SILVA, M. B. P. da; RIBEIRO, C. A. G.; GUIMARAES, C. T.; GEZAN, S. A.; GUIMARAES, L. J. M. A multi-environment trials diallel analysis provides insights on the inheritance of fumonisin contamination resistance in tropical maize. Euphytica, Dordrecht, v. 211, n. 3, p. 277-285, 2016| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 12. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARAES, C. T.; MAGALHAES, J. V. de; GUIMARAES, P. E. de O.; CARNEIRO, N. P.; PORTUGAL, A. F.; BASTOS, E. A.; CARDOSO, M. J.; ANONI, C. de O.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. Estimating genotype X environment interaction for and genetic correlations among drought tolerance traits in maize via factor analytic multiplicative mixed models. Crop Science, Madison, v. 58, p. 72-83, Jan. 2018. Publicado online em 30 out. 2017.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 12 | |
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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. |
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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Circulação/Nível: |
A - 1 |
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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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