|
|
Registro Completo |
Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
22/07/1993 |
Data da última atualização: |
12/12/2006 |
Autoria: |
FRANCA NETO, J. de B.; KRZYZANOWSKI, F. C. |
Afiliação: |
EMBRAPA-CNPSo, Cx. Postal 1061, CEP 86001-970, Londrina, PR. |
Título: |
Tratamento de sementes de soja e trigo com produtos biologicos. |
Ano de publicação: |
1993 |
Fonte/Imprenta: |
In: EMBRAPA. Centro Nacional de Pesquisa de Soja (Londrina, PR). Resultados de pesquisa de soja 1989/90. Londrina, 1993. |
Páginas: |
p.429-431. |
Série: |
(EMBRAPA-CNPSo. Documentos, 58). |
Idioma: |
Português |
Palavras-Chave: |
Brasil; Seed; Soybean; Treatment. |
Thesagro: |
Semente; Soja; Tratamento; Trigo. |
Thesaurus Nal: |
Brazil; wheat. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00745naa a2200265 a 4500 001 1452458 005 2006-12-12 008 1993 bl uuuu u00u1 u #d 100 1 $aFRANCA NETO, J. de B. 245 $aTratamento de sementes de soja e trigo com produtos biologicos. 260 $c1993 300 $ap.429-431. 490 $a(EMBRAPA-CNPSo. Documentos, 58). 650 $aBrazil 650 $awheat 650 $aSemente 650 $aSoja 650 $aTratamento 650 $aTrigo 653 $aBrasil 653 $aSeed 653 $aSoybean 653 $aTreatment 700 1 $aKRZYZANOWSKI, F. C. 773 $tIn: EMBRAPA. Centro Nacional de Pesquisa de Soja (Londrina, PR). Resultados de pesquisa de soja 1989/90. Londrina, 1993.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Soja (CNPSO) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
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. |
|
|