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| 3. |  | PRAGANA, R. B.; NÓBREGA, R. S. A.; RIBEIRO, M. R.; LUSTOSA FILHO, J. F. Atributos biológicos e dinâmica da matéria orgânica em latossolos amarelos na região do Cerrado piauiense sob sistema plantio direto Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 36, n. 3, p. 851-858, maio/jun. 2012. Título em Inglês: Effect of cropping systems on biological properties and organic matter dynamics of yellow oxisols in the Cerrado region of Piauí, Brazil.| Biblioteca(s): Embrapa Solos / UEP-Recife. |
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| 5. | | MOREIRA, F. M. de S.; NOBREGA, R. S. A.; CARVALHO, F. de; SILVA, K. da. Bactérias associativas fixadoras de nitrogênio atmosférico. In: MOREIRA, F. M. S. de; CARES, J. E.; ZANETTI, R.; STÜMER, S. L. (Ed.). O ecossistema solo: componentes, relaçãoes ecológicas e efeitos na produção vegetal.Lavras : UFLA, 2013| Biblioteca(s): Embrapa Roraima. |
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| 6. | | MOREIRA, F. M. de S.; SILVA, K. da; NOBREGA, R. S. A.; CARVALHO, F, de. Bactérias diazotróficas associativas: diversidade, ecologia e potencial de aplicações. Comunicata Scientiae, v. 1, n.2, p. 74-99, 2010.| Biblioteca(s): Embrapa Roraima. |
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| 9. | | MOREIRA, F. M. de S.; NOBREGA, R. S. A.; JESUS, E. da C.; FERREIRA, D. F.; PEREZ, D. V. Differentiation in the fertility of Inceptisols as related to land use in the upper Solimões river region, western Amazon. Science of the Total Environment, v. 408, n. 2, p. 349-355, Dec. 2009.| Biblioteca(s): Embrapa Solos. |
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| 10. | | JALA, I. M.; SILVA, C. C. da; SAMPAIO FILHO, J. S.; OLIVEIRA, E. J. de; NÓBREGA, R. S. A. Seedlings of cassava varieties are responsive to organic fertilization. Semina: Ciências Agrárias, Londrina, v. 40, n. 5, suplemento 1, p. 2151-2164, 2019.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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| 11. | | SILVA, K. da; NOBREGA, R. S. A.; LIMA, A. S.; BARBERI, A.; MOREIRA, F. M. de S. Density and diversity of diazotrophic bacteria isolated from Amazonian soils using N-free semi-solid media. Sci. Agric., v.68, n.5, p.518-525, Sep./Oct., 2011| Biblioteca(s): Embrapa Roraima. |
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| 12. | | SOUZA, P. S. S.; NASCIMENTO, R. de C.; SILVA, T. R. da; NÓBREGA, R. S. A.; FERNANDES JUNIOR, P. I. Eficiência agronômica de bactérias promotoras de crescimento vegetal nativas do Semiárido na produtividade de milho BRS Gorutuba. In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA SEMIÁRIDO, 13., 2018, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2018. p. 305-309. (Embrapa Semiárido. Documentos, 283).| Biblioteca(s): Embrapa Semiárido. |
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| 13. | | JESUS, A. A. de; COSTA, E. M. da; NÓBREGA, R. S. A.; DIÓGENES, L. C.; NÓBREGA, J. C. A. Crescimento e nodulação de Enterolobium contortisiliquum cultivado em solos de diferentes sistemas de uso no Sudoeste do Piauí. Pesquisa Florestal Brasileira, Colombo, v. 37, n. 92, p. 545-553, out./dez. 2017.| Biblioteca(s): Embrapa Florestas. |
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| 14. | | SOUSA, D. C. de; ROSA, J. D.; MEDEIROS, J. C.; BOECHAT, C. L.; NÓBREGA, R. S. A.; SOUZA, H. A. de; SAGRILO, E. Microbial indicators of soil quality and soybean yield in agricultural production system using cover crops under no-tillage. Australian Journal of Crop Science, v. 17, n. 6, p. 507-515, 2023.| Biblioteca(s): Embrapa Meio-Norte. |
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| 15. | | DIÓGENES, L. C.; NÓBREGA, J. C. A.; NÓBREGA, R. S. A.; ANDRADE JUNIOR, A. S. de; PRAGANA, R. B.; MATIAS, S. S. R. Microbial attributes and carbon and nitrogen stocks in Latosol under irrigated monocropping and intercropping. Revista de Ciências Agrárias, Belém, PA, v. 56, n. 2, p. 106-111, abr./jun. 2013.| Biblioteca(s): Embrapa Meio-Norte. |
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| 16. | | NÓBREGA, R. S. A.; FERREIRA, P. A. A.; SANTOS, J. G. D. dos; VILAS BOAS, R. C.; NÓBREGA, J. C. A.; MOREIRA, F. M. de S. Efeito do composto de lixo urbano e calagem no crescimento inicial de mudas de Enterolobium contortisiliquum (Vell.) Morong. Scientia Forestalis, Piracicaba, v. 36, n. 79, p 181-189, set. 2008.| Biblioteca(s): Embrapa Florestas. |
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| 17. | | NÓBREGA, R. S. A.; PAULA, A. M. de; VILAS BOAS, R. C.; NÓBREGA, J. C. A.; MOREIRA, F. M. de S. Parâmetros morfológicos de mudas de Sesbania virgata (Caz.) Pers e de Anadenanthera peregrina (L.) cultivadas em substrato fertilizado com composto de lixo urbano. Revista Árvore, Viçosa, v. 32, n. 3, p. 587-597, maio/jun. 2008.| Biblioteca(s): Embrapa Florestas. |
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| 18. | | DIÓGENES, L. C.; NÓBREGA, J. C. A.; NÓBREGA, R. S. A.; ANDRADE JUNIOR, A. S. de; SILVA, J. L. da; MATIAS, S. S. R.; SANTOS, G. G. Resistência à penetração e atributos químicos em um latossolo do Piauí sob monocultivos e consórcio de gramíneas irrigados. Irriga, Botucatu, Ed. esp. Grandes Culturas, p. 181-195, 2016.| Biblioteca(s): Embrapa Meio-Norte. |
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| 19. | | LIRA JÚNIOR, M. A.; FRACETTO, G. G. M.; ARAÚJO, A. S. F.; FRACETTO, F. J. C.; NOBREGA, R. S. A.; SILVA, K. da; GALDINO, A. C. Rhizobial Diversity for Tropical Pulses and Forage and Tree Legumes in Brazil. In: KHAN, M. S.; MUSARRAT, J.; ZAIDI, A. (Ed.). Microbes for Legume Improvement. Springer eBooks, 2017. p. 135-151.| Biblioteca(s): Embrapa Roraima. |
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| 20. | | CARVALHO, B. R.; SILVA, T. R. da; SANTOS, J. M. R. dos; NASCIMENTO, R. de C.; NÓBREGA, R. S. A.; FERNANDES JUNIOR, P. I. Eficiência agronômica de novas bactérias diazotróficas isoladas do milho (Zea mays L.). In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA SEMIÁRIDO, 12., 2017, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2017. p. 313-319. (Embrapa Semiárido. Documentos, 279).| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 35 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
16/12/2019 |
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Data da última atualização: |
16/12/2019 |
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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: |
SILVEIRA, L. S.; MARTINS FILHO, S.; AZEVEDO, C. F.; BARBOSA, E. C.; RESENDE, M. D. V. de; TAKAHASHI, E. K. |
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Afiliação: |
L. S. Silveira, UFV; S. Martins Filho, UFV; C. F. Azevedo, UFV; E. C. Barbosa, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; E. K. Takahashi, CENIBRA. |
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Título: |
Bayesian models applied to genomic selection for categorical traits. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 4: gmr18490, 2019. 10 p. |
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DOI: |
10.4238/gmr18490 |
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Idioma: |
Inglês |
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Conteúdo: |
We compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (GBLUP). The BGLMM had the best CVER (0.29 against 0.32 and 0.47 against 0.51 for 2 and 4 categories, respectively), BLMM had a three times shorter computational time, and though BLMM is not the most appropriate model for handling categorical data, this model presented similar responses to BGLMM. Thus, we consider it as an appropriate alternative for categorical data modeling. MenosWe compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (G... Mostrar Tudo |
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Palavras-Chave: |
Bayesian inference; Statistical methods. |
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Thesagro: |
Melhoramento Genético Vegetal. |
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Thesaurus NAL: |
Genetic improvement; Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02649naa a2200253 a 4500
001 2116962
005 2019-12-16
008 2019 bl uuuu u00u1 u #d
024 7 $a10.4238/gmr18490$2DOI
100 1 $aSILVEIRA, L. S.
245 $aBayesian models applied to genomic selection for categorical traits.$h[electronic resource]
260 $c2019
520 $aWe compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (GBLUP). The BGLMM had the best CVER (0.29 against 0.32 and 0.47 against 0.51 for 2 and 4 categories, respectively), BLMM had a three times shorter computational time, and though BLMM is not the most appropriate model for handling categorical data, this model presented similar responses to BGLMM. Thus, we consider it as an appropriate alternative for categorical data modeling.
650 $aGenetic improvement
650 $aPlant breeding
650 $aMelhoramento Genético Vegetal
653 $aBayesian inference
653 $aStatistical methods
700 1 $aMARTINS FILHO, S.
700 1 $aAZEVEDO, C. F.
700 1 $aBARBOSA, E. C.
700 1 $aRESENDE, M. D. V. de
700 1 $aTAKAHASHI, E. K.
773 $tGenetics and Molecular Research$gv. 18, n. 4: gmr18490, 2019. 10 p.
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