| |
|
|
| Registros recuperados : 74 | |
| 41. |  | CERESINO, E. B.; MINIM, V. P. R.; QUEIROZ, V. A. V.; PACHECO, C. A. P.; VIANA, J. M. S. Aceitabilidade de diferentes cultivares de milho de pipoca. In: CONGRESSO BRASILEIRO DE CIÊNCIA E TECNOLOGIA DOS ALIMENTOS, 21.; SEMINÁRIO LATINOAMERICANO E DO CARIBE DE CIÊNCIA E TECNOLOGIA DE ALIMENTOS, 15., 2008, Belo Horizonte. Anais... Belo Horizonte: Sociedade Brasileira de Ciência e Tecnologia de Alimentos, 2008. 1 CD-ROM.| Biblioteca(s): Embrapa Milho e Sorgo. |
|    |
| 44. | | MACHADO, G. M. E.; REGAZZI, A. J.; VIANA, J. M. S.; CRUZ, C. D.; GRANATE, M. J. Estimação de parâmetros genéticos de uma população amazônica de cupuaçuzeiro (Theobroma grandiflorum (Wild ex Spreng) Schum). Revista Ceres, Viçosa, v. 49, n. 281, p. 13-27, 2002.| Biblioteca(s): Embrapa Florestas. |
| |
| 45. | | COIMBRA, R. R.; MIRANDA, G. V.; MURAKAMI, D.; VIANA, J. M. S.; CRUZ, C. D.; NUNES, H. V. Estimativas de parametros geneticos e predicao de ganhos para a populacao DFT1r Ribeirao de milho-pipoca. In: CONGRESSO NACIONAL DE MILHO E SORGO, 23., 2000, Uberlandia, MG. A inovacao tecnologica e a competitividade no contexto dos mercados globalizados: [anais]... Sete Lagoas: ABMS / Embrapa Milho e Sorgo / Universidade Federal de Uberlandia, 2000. CD-ROM.| Biblioteca(s): Embrapa Hortaliças. |
| |
| 46. | | VIANA, J. M. S.; CONDÉ, A. B. T.; ALMEIDA, R. V. de; SCAPIM, C. A.; VALENTINI, L. Relative importance of per se and topcross performance in the selection of popcorn S3 families. Crop Breeding and Applied Biotechnology, Londrina, v. 7, n. 1, p. 74-81, June 2007.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 47. | | VALENTE, M. S.; VIANA, J. M. S.; RESENDE, M. D. V. de; SILVA, F. F. e; LOPES, M. T. G. Seleção genômica para melhoramento vegetal com diferentes estruturas populacionais. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 11, p. 1857-1867, nov. 2016. Título em inglês: Genomic selection for plant breeding with different population structures.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
| |
| 48. | | BONOMO, P.; CRUZ, C. D.; VIANA, J. M. S.; PEREIRA, A. A.; OLIVEIRA, V. R.; CARNEIRO, P. C. S. Seleção antecipada de progênies de café descendentes de "híbrido de Timor" x "Catuaí Amarelo" e "Catuaí Vermelho". Acta Scientiarum Agronomy, Maringá, v. 26, n. 1, p. 97-96, 2004.| Biblioteca(s): Embrapa Hortaliças. |
| |
| 49. | | SAWAZAKI, E.; GALLO, P. B.; CASTRO, J. L.; SAWAZAKI, H. E.; DUARTE, A.; VIANA, J. M. S.; MIRANDA, G. V.; PACHECO, C. A. P. Comportamento de genótipos de milho pipoca na região Centro-Sul do Estado de São Paulo. In: CONGRESSO NACIONAL DE MILHO E SORGO, 26.; SIMPÓSIO BRASILEIRO SOBRE A LAGARTA-DO-CARTUCHO, SPODOPTERA FRUGIPERDA, 2.; SIMPÓSIO SOBRE COLLETOTRICHUM GRAMINICOLA, 1., 2006, Belo Horizonte, Inovação para sistemas integrados de produção: trabalhos apresentados. [Sete Lagoas]: ABMS, 2006. 1 CD-ROM.| Biblioteca(s): Embrapa Milho e Sorgo. |
| |
| 50. | | SAWAZAKI, E.; GALLO, P. B.; CASTRO, J. L.; SAWAZAKI, H. E.; DUARTE, A.; VIANA, J. M. S.; MIRANDA, G. V.; PACHECO, C. A. P. Comportamento de genótipos de milho pipoca na região Centro-Sul do Estado de São Paulo. In: CONGRESSO NACIONAL DE MILHO E SORGO, 26.; SIMPÓSIO BRASILEIRO SOBRE A LAGARTA-DO-CARTUCHO, SPODOPTERA FRUGIPERDA, 2.; SIMPÓSIO SOBRE COLLETOTRICHUM GRAMINICOLA, 1., 2006, Belo Horizonte. Inovação para sistemas integrados de produção: resumos. Sete Lagoas: ABMS: Embrapa Milho e Sorgo, 2006. p. 257.| Biblioteca(s): Embrapa Milho e Sorgo. |
| |
| 51. | | TRINDADE, R. dos S.; RODRIGUES, R.; AMARAL JÚNIOR, A. T. do; GONÇALVES, L. S. A.; VIANA, J. M. S.; SUDRÉ, C. P. Combining ability for common bacterial blight resistance in snap and dry bean (Phaseolus vulgaris L.). Acta Scientiarum. Agronomy, Maringá, v. 37, n. 1, p. 37-43, jan./mar. 2015.| Biblioteca(s): Embrapa Milho e Sorgo. |
| |
| 52. | | LIMA, L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; OLIVEIRA, E. J. de. Triple categorical regression for genomic selection: application to cassava breeding. Scientia Agricola, v. 76, n. 5, p. 368-375, Sept./Oct. 2019.| Biblioteca(s): Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
| |
| 53. | | COIMBRA, R. R.; MIRANDA, G. V.; VIANA, J. M. S.; CRUZ, C. D.; MURAKAMI, D. M.; SOUZA, L. V. de; FIDELIS, R. R. Estimation of genetic parameters and prediction of gains for DFT1-Ribeirão popcorn population. Crop Breeding and Applied Biotechnology, Londrina, v. 2, n. 1, p. 33-37, Mar. 2002.| Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Hortaliças. |
| |
| 55. | | MARIGUELE, K. H.; RESENDE, M. D. V. de; VIANA, J. M. S.; SILVA, F. F. e; SILVA, P. S. L. de; KNOP, F. de C. Métodos de análise de dados longitudinais para o melhoramento genético da pinha. Pesquisa Agropecuária Brasileira, Brasília, v. 46, n. 12, p. 1657-1664, dez. 2011.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
| |
| 56. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F.; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; OLIVEIRA, E. J. de. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program. Genetics and Molecular Research, v. 15, n. 4, gmr.15048838, Oct. 2016.| Biblioteca(s): Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
| |
| 57. | | 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. |
| |
| 59. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; NASCIMENTO, M.; VIANA, J. M. S.; VALENTE, M. S. F. Population structure correction for genomic selection through eigenvector covariates. Crop Breeding and Applied Biotechnology, Viçosa, v. 17, n. 4, p.350-358, Oct./Dec. 2017.| Biblioteca(s): Embrapa Florestas. |
| |
| 60. | | LANES, E. C. M.; VIANA, J. M. S.; PAES, G. P.; PAULA, M. F. B.; MAIA, C.; CAIXETA, E. T.; MIRANDA, G. V. Population structure and genetic diversity of maize inbreds derived from tropical hybrids. Genetics and Molecular Research, v. 13, n. 3, p. 7365-7376, 2014.| Biblioteca(s): Embrapa Café. |
|  |
| Registros recuperados : 74 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
03/01/2018 |
|
Data da última atualização: |
11/01/2018 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. |
|
Afiliação: |
M. Nascimento, UFV; F. F. e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; C. D. Cruz, UFV; A. C. C. Nascimento, UFV; J. M. S. Viana, UFV; C. F. Azevedo, UFV; L. M. A. Barroso, UFV. |
|
Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
|
Páginas: |
12 p. |
|
Idioma: |
Inglês |
|
Conteúdo: |
Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. MenosGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved be... Mostrar Tudo |
|
Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
|
Thesagro: |
Estatística. |
|
Thesaurus NAL: |
Marker-assisted selection; Simulation models; Statistics. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
|
|
Marc: |
LEADER 02634naa a2200313 a 4500
001 2084109
005 2018-01-11
008 2017 bl uuuu u00u1 u #d
100 1 $aNASCIMENTO, M.
245 $aRegularized quantile regression applied to genome-enabled prediction of quantitative traits.$h[electronic resource]
260 $c2017
300 $a12 p.
520 $aGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.
650 $aMarker-assisted selection
650 $aSimulation models
650 $aStatistics
650 $aEstatística
653 $aGenomic selection
653 $aRegularized regression
653 $aSeleção genômica
653 $aSNP effects
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aVIANA, J. M. S.
700 1 $aAZEVEDO, C. F.
700 1 $aBARROSO, L. M. A.
773 $tGenetics and Molecular Research$gv. 16, n. 1, gmr16019538, 2017.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
