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Registro Completo |
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
12/01/2005 |
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Data da última atualização: |
12/01/2005 |
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Autoria: |
BUENO, A. R. O.; CURADO, F. F. |
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Afiliação: |
UCDB (Corumbá, MS); Embrapa Pantanal (Corumbá, MS). |
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Título: |
Considerações sobre a realidade sócio-econômica dos assentamentos rurais Paiolzinho e Tamarineiro II Sul, Corumbá. |
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Ano de publicação: |
2004 |
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Fonte/Imprenta: |
In: SIMPÓSIO SOBRE RECURSOS NATURAIS E SÓCIO-ECONÔMICOS DO PANTANAL, 4., 2004, Corumbá, MS. Sustentabilidade regional: anais. Corumbá: Embrapa Pantanal: UCDB: UFMS: SEBRAE-MS, 2004. (CD-ROM). |
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Idioma: |
Português |
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Conteúdo: |
Este trabalho evidencia alguns resultados preliminares de um levantamento sócioeconômico e ambiental dos assentamentos Paiolzinho e Tamarineiro II intitulado
"Diagnóstico Participativo de Agroecossistemas dos Assentamentos Rurais de Corumbá
(MS)", coordenado pela Embrapa Pantanal, cujo objetivo principal é promover o
levantamento participativo de informações acerca da realidade dos agroecossistemas de
assentamentos rurais e da situação sócio-econômica das famílias assentadas. As
informações foram obtidas mediante a aplicação de questionários, visitas técnicas, reuniões e outras dinâmicas participativas conduzidas junto às comunidades, envolvendo as formas de organização existentes. |
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Palavras-Chave: |
Assentamento rural; Desenvolvimento local; Diagnóstico participativo. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01400naa a2200169 a 4500
001 1811519
005 2005-01-12
008 2004 bl uuuu u00u1 u #d
100 1 $aBUENO, A. R. O.
245 $aConsiderações sobre a realidade sócio-econômica dos assentamentos rurais Paiolzinho e Tamarineiro II Sul, Corumbá.
260 $c2004
520 $aEste trabalho evidencia alguns resultados preliminares de um levantamento sócioeconômico e ambiental dos assentamentos Paiolzinho e Tamarineiro II intitulado "Diagnóstico Participativo de Agroecossistemas dos Assentamentos Rurais de Corumbá (MS)", coordenado pela Embrapa Pantanal, cujo objetivo principal é promover o levantamento participativo de informações acerca da realidade dos agroecossistemas de assentamentos rurais e da situação sócio-econômica das famílias assentadas. As informações foram obtidas mediante a aplicação de questionários, visitas técnicas, reuniões e outras dinâmicas participativas conduzidas junto às comunidades, envolvendo as formas de organização existentes.
653 $aAssentamento rural
653 $aDesenvolvimento local
653 $aDiagnóstico participativo
700 1 $aCURADO, F. F.
773 $tIn: SIMPÓSIO SOBRE RECURSOS NATURAIS E SÓCIO-ECONÔMICOS DO PANTANAL, 4., 2004, Corumbá, MS. Sustentabilidade regional: anais. Corumbá: Embrapa Pantanal: UCDB: UFMS: SEBRAE-MS, 2004. (CD-ROM).
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Registro original: |
Embrapa Pantanal (CPAP) |
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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
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.
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