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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. |
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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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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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Registro original: |
Embrapa Florestas (CNPF) |
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| Registros recuperados : 8 | |
| 1. |  | BARBOSA, E. C. Choice-based conjoint analysis: um enfoque Bayesiano. Viçosa, MG, 2015. 119 f. Dissertação (Mestrado em Estatística aplicada a Biometria) - Universidade Federal de Viçosa, MG, 2015. Orientador: Carlos Henrique Osório Silva, UFV; Co-orientador: Moysés Nascimento; Co-Orientador: Rosires Deliza, CTAA.| Tipo: Orientação de Tese de Pós-Graduação |
| Biblioteca(s): Embrapa Agroindústria de Alimentos. |
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| 3. | | SILVEIRA, M. C. T. da; GENRO, T. C. M.; KÖPP, M. M.; YOKOO, M. J. I.; BARBOSA, E. C. Caracterização da estrutura vertical de pastagem natural sob diferentes níveis de intensificação. In: SIMPÓSIO BRASILEIRO DE AGROPECUÁRIA SUSTENTÁVEL, 7.; CONGRESSO INTERNACIONAL DE AGROPECUÁRIA SUSTENTÁVEL, 4., 2015, Viçosa, MG. Estratégias para a sustentabilidade da cadeia agropecuária: anais de resumos expandidos. Viçosa, MG: UFV, 2015. p. 595-598.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Pecuária Sul. |
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| 4. | | BARBOSA, E. C.; SILVA, C. H. O.; NASCIMENTO, M.; SILVA, F. F. e; MINIM, V. P. R.; DELIZA, R.; SILVA, S. M. Della L. Choice-based conjoint analysis: um enfoque bayesiano. Revista Brasileira de Biometria, Lavras, v.36, n.1, 2018. 19 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 3 |
| Biblioteca(s): Embrapa Agroindústria de Alimentos. |
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| 5. | | SANTOS, V. A. dos; SOUZA, J. S. de; CHAGAS, E. A.; ARAÚJO, M. da C. da R.; BARBOSA, E. C.; SILVA, D. L. Formação de mudas de camu camu utilizando enxertia precoce. In: SIMPÓSIO INTERNACIONAL DE BOTÂNICA APLICADA, 2.; SIMPÓSIO NACIONAL DE FRUTÍFERAS E ORNAMENTAIS DO NORTE E NORDESTE, 2., 2013, Manaus. Anais... Manaus: Ufam, 2013.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Roraima. |
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| 6. | | BARBOSA, E. C. V.; PAPA, G.; MELO, W. L. de B.; JORGE, L. A. de C.; SILVA, H. R.; ROMERO, C. W. S. Radiometria na avaliação da eficiência da reflexão do ultravioleta por diferentes mulching no controle do tripes-do-tomateiro, Frankliniella schultzei (Trybom). In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos, SP. Anais... São José dos Campos: INPE, 2017. 3276-3282| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Instrumentação. |
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| 7. | | BARBOSA, E. C. V.; PAPA, G.; MELO, W. L. de B.; JORGE, L. A. de C.; SILVA, H. R.; ROMERO, C. W. S. Radiometria na avaliação da eficiência da reflexão do ultravioleta por diferentes mulching no controle do tripes-do-tomateiro, Frankliniella schultzei (Trybom). Brazilian Journal of Development, v. 6, n. 6, 2020. 39316 - 39325| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 5 |
| Biblioteca(s): Embrapa Instrumentação. |
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| 8. | | PINTO, J. F. N.; REIS, E. F. DOS; FALEIRO, F. G.; BARBOSA, E. C. C.; NUNES, H. F.; PINTO, J. F. N. Seleção de descritores vegetativos para caracterização de acessos de guariroba (Syagrus oleracea (Mart.) Becc.). Revista Brasileira de Fruticultura, Jaboticabal, v. 32, n. 3, p. 832-840, set. 2010.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 8 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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