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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura; Embrapa Tabuleiros Costeiros. |
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Data corrente: |
21/01/2008 |
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Data da última atualização: |
19/03/2008 |
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Tipo da produção científica: |
Documentos |
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Autoria: |
CARVALHO, H. W. L. de; FUKUDA, W. M. G.; RIBEIRO, F. E.; OLIVEIRA, I. R. de; FUKUDA, C.; MOREIRA, M. A. B.; SILVA, V. S.; LIMA, N, R. S.; LEÃO, K. R. B.; AMORIM, J, R. A.; RODRIGUES, A. R. dos S.; OLIVEIRA, V. D. de; SOUZA, E. M. de; RIBEIRO, S. S. |
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Afiliação: |
Hélio Wilson Lemos de Carvalho, Embrapa Tabuleiros Costeiros; Wânia Maria Gonçalves Fukuda, Embrapa Mandioca e Fruticultura Tropical; Francisco Elias Ribeiro, Embrapa Tabuleiros Costeiros; Ivênio Rubens de Oliveira, Embrapa Tabuleiros Costeiros; Chigeru Fukuda, Embrapa Mandioca e Fruticultura Tropical; Marcos Antônio Barbosa Moreira, Embrapa Tabuleiros Costeiros/EMPARN; Vanderlei Santos Silva, Embrapa Mandioca e Fruticultura Tropical; Neusa Rosani Stahlschmidt Lima, Embrapa Tabuleiros Costeiros/DEAGRO; Kátia Regina Barbosa Leão, EBDA; Júlio Roberto Araújo Amorim, Embrapa Tabuleiros Costeiros; Agna Rita dos Santos Rodruigues, (bolsista) Embrapa Tabuleiros Costeiros; Vanice Dias de Oliveira, (bolsista) Embrapa Tabuleiros Costeiros; Evanildes Menezes de Souza, (estagiária) Embrapa Tabuleiros Costeiros; Sandra Santos Ribeiro, (estagiária) Embrapa Tabuleiros Costeiros. |
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Título: |
Rede de adaptação de cultivares de aipim e mandioca para o nordeste brasileiro. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Aracaju: Embrapa Tabuleiros Costeiros, 2007. |
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Páginas: |
29 p. |
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Série: |
(Embrapa Tabuleiros Costeiros. Documentos, 100). |
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Idioma: |
Português |
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Conteúdo: |
Peso da parte aérea, Nossa Senhora das Dores; Peso de raízes, Nossa Senhora das Dores; Teor de matéria seca de raíz, Nossa Senhora das Dores; Teor de amido, Nossa Senhora das Dores; Peso da parte aérea, Lagarto; Peso de raízes, Lagarto; Teor de matéria seca de raíz, Lagarto; Teor de amido, lagarto; Altura da planta, Lagarto; Altura da 1º ramificação, Lagarto; peso da parte aérea, Umbaúba; Peso de raíz, Umbaúba; Teor de matéria seca de raíz, Umbaúba; Teor de amido, Umbaúba; Altura da planta, Umbaúba; Altura da 1º ramificação, Umbaúba; comprimento de raíz, Umbaúba; Ensaio de variedades e híbridos de mandioca; Avaliação de cultivares de mandioca no período 2004/2006; Peso da parte aérea, Nossa Senhora das Dores; peso de raízes, Nossa Senhora das Dores; Índice de colheita, Nossa Senhora das Dores; Teor de matéria seca de raíz, Nossa Senhora das Dores; Teor de amido, Nossa Senhora das Dores |
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Palavras-Chave: |
Aipim; Economia - Nordeste - Brasil. |
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Thesagro: |
Economia; Hibrido; Mandioca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CPATC/19584/1/Doc-100_r.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPMF/24320/1/Doc-100_r.pdf
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Marc: |
LEADER 01919nam a2200349 a 4500
001 1372230
005 2008-03-19
008 2007 bl uuuu u0uu1 u #d
100 1 $aCARVALHO, H. W. L. de
245 $aRede de adaptação de cultivares de aipim e mandioca para o nordeste brasileiro.
260 $aAracaju: Embrapa Tabuleiros Costeiros$c2007
300 $a29 p.
490 $a(Embrapa Tabuleiros Costeiros. Documentos, 100).
520 $aPeso da parte aérea, Nossa Senhora das Dores; Peso de raízes, Nossa Senhora das Dores; Teor de matéria seca de raíz, Nossa Senhora das Dores; Teor de amido, Nossa Senhora das Dores; Peso da parte aérea, Lagarto; Peso de raízes, Lagarto; Teor de matéria seca de raíz, Lagarto; Teor de amido, lagarto; Altura da planta, Lagarto; Altura da 1º ramificação, Lagarto; peso da parte aérea, Umbaúba; Peso de raíz, Umbaúba; Teor de matéria seca de raíz, Umbaúba; Teor de amido, Umbaúba; Altura da planta, Umbaúba; Altura da 1º ramificação, Umbaúba; comprimento de raíz, Umbaúba; Ensaio de variedades e híbridos de mandioca; Avaliação de cultivares de mandioca no período 2004/2006; Peso da parte aérea, Nossa Senhora das Dores; peso de raízes, Nossa Senhora das Dores; Índice de colheita, Nossa Senhora das Dores; Teor de matéria seca de raíz, Nossa Senhora das Dores; Teor de amido, Nossa Senhora das Dores
650 $aEconomia
650 $aHibrido
650 $aMandioca
653 $aAipim
653 $aEconomia - Nordeste - Brasil
700 1 $aFUKUDA, W. M. G.
700 1 $aRIBEIRO, F. E.
700 1 $aOLIVEIRA, I. R. de
700 1 $aFUKUDA, C.
700 1 $aMOREIRA, M. A. B.
700 1 $aSILVA, V. S.
700 1 $aLIMA, N, R. S.
700 1 $aLEÃO, K. R. B.
700 1 $aAMORIM, J, R. A.
700 1 $aRODRIGUES, A. R. dos S.
700 1 $aOLIVEIRA, V. D. de
700 1 $aSOUZA, E. M. de
700 1 $aRIBEIRO, S. S.
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Registro original: |
Embrapa Tabuleiros Costeiros (CPATC) |
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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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Embrapa Florestas (CNPF) |
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