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| Registros recuperados : 40 | |
| 3. |  | AZEVEDO, D. M. P. de; SANTOS, J. W. dos; DIAS, J. M. : JERÔNIMO, J. F. Efeito da densidade de plantio na produção e nas características da fibra de genótipos de algodoeiro herbáceo, no sudoeste do Estado da Bahia. Revista Brasileira de Oleaginosas e Fibrosas, Campina Grande, v.7, n.1, p.665-672, 2003,| Biblioteca(s): Embrapa Algodão. |
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| 7. | | FRANÇA, P. R. C.; SILVA, O. R. R. F.; JERÔNIMO, J. F.; ALMEIDA, F. A. C. Desempenho operacional de um descaroçador móvel em lotes de algodão com diferentes qualidades. In: BELTRAO, N. E. de M.; SILVA, C. A. D. da; SANTOS, J. W. dos; LIMA. M. M. de A.; SILVA, I. C. da. ENCONTRO DE PRODUÇÃO CIENTÍFICA DA EMBRAPA ALGODÃO - EPC 2012, 7., 2012, Campina Grande. [Resumos...]. Campina Grande: Embrapa Algodão, 2012. 1 p.| Biblioteca(s): Embrapa Algodão. |
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| 9. | | FRANÇA, P. R. C. DE; SILVA, O. R. R. F.; ALMEIDA, F. DE A. C.; JERÔNIMO, J. F. Secagem de algodão em rama no desempenho operacional de uma miniusina de beneficiamento. In: CONGRESSO BRASILEIRO DE MAMONA, 6.; SIMPÓSIO INTERNACIONAL DE OLEAGINOSAS ENERGÉTICAS, 3., 2014, Fortaleza. Energia e segurança alimentar na agricultura familiar: anais. Campina Grande, PB: Embrapa Algodão, 2014. p. 182| Biblioteca(s): Embrapa Algodão. |
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| 12. | | QUEIROGA, V. de P.; CASTRO, L. B. DE Q.; GOMES, J. P.; JERÔNIMO, J. F.; PEDROZA, J. P. Qualidade de sementes de algodão armazenadas em função de diferentes cultivares e teores de água. Revista Caatinga, v. 22, n. 4, p. 136-144, out./dez., 2009.| Biblioteca(s): Embrapa Algodão. |
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| 15. | | SOUSA, E. I F. de; MATOS, A. P. de; ANDRADE, G.; MIRANDA, K.; BRANDÃO, M.; FERRREIRA, H.; JERONIMO, J. A cultura do abacaxi em São Domingos do Maranhão. In: SIMPÓSIO BRASILEIRO DA CULTURA DO ABACAXI, 5., 2013, Palmas. Produção e qualidade com tecnologia e sustentabilidade: anais. Palmas: Secretaria da Agricultura e Pecuária do Estado do Tocantins, 2013. 1 CD-ROM.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 16. | | JERÔNIMO, J. F.; FRANÇA, P. R. C. de; SILVA, O. R. R. F.; ALMEIDA, F. de A. C.; SOFIATTI, V. Comparação dos descaroçadores de 25 e 50 serras na qualidade tecnológica da fibra de algodão. CONGRESSO BRASILEIRO DE MAMONA, 5.; SIMPÓSIO INTERNACIONAL DE OLEAGINOSAS ENERGÉTICAS, 2.; FÓRUM CAPIXABA DE PINHÃO-MANSO, 1., 2012, Guarapari. Desafios e Oportunidades: anais. Campina Grande: Embrapa Algodão, 2012. p. 338| Biblioteca(s): Embrapa Algodão. |
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| Registros recuperados : 40 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
03/01/2018 |
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Data da última atualização: |
11/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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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. |
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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. |
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Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
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Páginas: |
12 p. |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
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Thesagro: |
Estatística. |
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Thesaurus NAL: |
Marker-assisted selection; Simulation models; Statistics. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
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Marc: |
LEADER 02634naa a2200313 a 4500
001 2084109
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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.
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Embrapa Florestas (CNPF) |
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