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| Registros recuperados : 90 | |
| 27. |  | AZEVEDO, C. F. de; MOURA, A. de A. A.; LOBO, R. N. B.; MODESTO, E. C.; MARTINS FILHO, R. Avaliação de fatores não genéticos sobre características de peso em bovinos Nelore e Guzerá no Estado do Rio Grande do Norte. Revista Ciência Agronômica, Fortaleza, v. 36, n. 2, p. 227-236, maio/ago., 2005.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 28. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; LOPES, P. S.; GUIMARÃES, S. E. F. Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 6, p. 619-626, jun. 2013.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 30. | | PEREIRA, V. C. S.; DIAS, R. V.; AZEVEDO, C. F. e O.; PEREIRA, I. S.; HONDA, L. S.; PINHEIRO, E. F. M.; CAMPOS, D. V. B. de. Agregação do solo sob diferentes usos e cobertura vegetal no bioma Cerrado. In: CONGRESSO LATINO-AMERICANO DE CIÊNCIA DO SOLO, 23.; CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 38., 2023, Florianópolis. Anais [...]. Florianópolis: Epagri, 2023. p. 949. Ref. ID 1339.| Biblioteca(s): Embrapa Solos. |
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| 33. | | 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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| 34. | | TEODORO, P. E.; FARIAS, F. J. C.; CARVALHO, L. P. de; RIBEIRO, L. P.; NASCIMENTO, M.; AZEVEDO, C. F.; CRUZ, C. D.; BHERING, L. L. Adaptability and stability of cotton genotypes regarding fiber yield and quality traits. Crop Science, v. 59, p. 518?524, March?April 2019.| Biblioteca(s): Embrapa Algodão. |
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| 35. | | COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. Determination of optimal number of independent components in yield traits in rice. Scientia Agricola, v. 79, n. 6, p. 1-8, 2022.| Biblioteca(s): Embrapa Café. |
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| 36. | | AZEVEDO, C. F.; SILVA, F. F. e; RIBEIRO, N. B.; SILVA, D. J. H. da; CECON, P. R.; BARILI, L. D.; PINHEIRO, V. R. Classificação multivariada de curvas de progresso da requeima do tomateiro entre acessos do Banco de Germoplasma de Hortaliças da UFV. Ciência Rural, Santa Maria, v. 42, n. 3, p. 414-417, mar. 2012.| Biblioteca(s): Embrapa Hortaliças. |
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| 38. | | AZEVEDO, C. F.; CARVALHO, I. R.; NASCIMENTO, M.; SILVA, J. A. G. da; NASCIMENTO, A, C. C.; CRUZ, C. D.; HUTH, C.; ALMEIDA, H. C. F. de. Informative prior distribution applied to linseed for the estimation of genetic parameters using a small sample size. Pesquisa Agropecuária Brasileira, v. 57, e02793, 2022. Título em português: Distribuição a priori informativa aplicada à linhaça para estimação de parâmetros genéticos com uso de tamanho amostral reduzido.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 39. | | PINHEIRO, V. R.; SILVA, F. F. e; GUIMARÃES, S. E. F.; RESENDE, M. D. V. de; LOPES, P. S.; CRUZ, C. D.; AZEVEDO, C. F. Mapeamento de QTL para características de crescimento de suínos por meio de modelos de regressão aleatória. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 2, p. 190-196, fev. 2013.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 40. | | LIMA L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; SUELA, M. M.; NASCIMENTO, M.; VIANA, J. M. S. New insights into genomic selection through population-based non-parametric prediction methods. Scientia Agricicola, v. 76, n. 4, p. 290-298, July/Aug. 2019.| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 90 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
24/08/2015 |
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Data da última atualização: |
25/02/2016 |
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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: |
AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. |
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Afiliação: |
Camila Ferreira Azevedo, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, UFV; José Marcelo Soriano Viana, UFV; Magno Sávio Ferreira Valente, UFV; Márcio Fernando Ribeiro Resende Jr, Florida Innovation Hub; Patricio Muñoz, University of Florida. |
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Título: |
Ridge, Lasso and Bayesian additive dominance genomic models. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
BMC Genetics, v. 16, art. 105, Aug. 2015. 13 p. |
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DOI: |
10.1186/s12863-015-0264-2 |
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Idioma: |
Inglês |
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Conteúdo: |
Background: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models. MenosBackground: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS ... Mostrar Tudo |
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Palavras-Chave: |
Bayesian methods; Dominance genomic models; Genética quantitativa; Lasso methods; Melhoramento genético; Modelo Bayesiano; Selection accuracy. |
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Thesagro: |
Parâmetro Genético. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/128510/1/2015-API-Deon-Ridge.pdf
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Marc: |
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024 7 $a10.1186/s12863-015-0264-2$2DOI
100 1 $aAZEVEDO, C. F.
245 $aRidge, Lasso and Bayesian additive dominance genomic models.$h[electronic resource]
260 $c2015
520 $aBackground: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.
650 $aParâmetro Genético
653 $aBayesian methods
653 $aDominance genomic models
653 $aGenética quantitativa
653 $aLasso methods
653 $aMelhoramento genético
653 $aModelo Bayesiano
653 $aSelection accuracy
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. F. e
700 1 $aVIANA, J. M. S.
700 1 $aVALENTE, M. S. F.
700 1 $aRESENDE JUNIOR, M. F. R.
700 1 $aMUÑOZ, P.
773 $tBMC Genetics$gv. 16, art. 105, Aug. 2015. 13 p.
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