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Registro Completo |
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
16/12/2019 |
Data da última atualização: |
16/12/2019 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SILVEIRA, L. S.; MARTINS FILHO, S.; AZEVEDO, C. F.; BARBOSA, E. C.; RESENDE, M. D. V. de; TAKAHASHI, E. K. |
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. |
Título: |
Bayesian models applied to genomic selection for categorical traits. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 4: gmr18490, 2019. 10 p. |
DOI: |
10.4238/gmr18490 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Bayesian inference; Statistical methods. |
Thesagro: |
Melhoramento Genético Vegetal. |
Thesaurus Nal: |
Genetic improvement; Plant breeding. |
Categoria do assunto: |
G Melhoramento Genético |
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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Registros recuperados : 104 | |
4. | | PIROVANI, V. D.; FANCELLI, M.; MOREIRA, B. M.; SILVEIRA, L. F. V.; PRATISSOLI, D. Azamora penicillana occurrence in sour passion fruit in the state of Minas Gerais. Revista Brasileira de Fruticultura, v.42, n.1, Jaboticabal, Epub Feb 21, 2020.Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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5. | | FERREIRA, F. S.; BARRETTO, F. L.; FABRES, A.; SILVEIRA, L. S.; CARVALHO, C. B. Cardiac markers in five different breeds of rabbits (Oryctolagus cuniculus Linnaeus, 1758) used for cardiovascular research. Pesquisa Veterinária Brasileira, Rio de Janeiro, v. 36, n. 8, p. 737-742, ago. 2016.Biblioteca(s): Embrapa Unidades Centrais. |
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6. | | QUEIRÓZ, M. A. de; SILVEIRA, L. M. da; LEITE, W. de M. Avaliação preliminar de acessos de melancia coletados na região de Vitória da Conquista-BA. Horticultura Brasileira, Brasilia, DF, v. 21, n. 2, jul. 2003. 1 CD-ROM. Suplemento. Edição de Resumos expandidos e palestras do 43. Congresso Brasileiro de Olericultura, Recife, jul. 2003.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Semiárido. |
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7. | | LOPES, C. W. G.; CORRÊA, I. C.; SILVA, P. C. da; SILVEIRA, L. F. da. Prevalência e intensidade de infestação de helmintos gastrintestinais em Ovis aries do Estado da Bahia. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 10, n. 8, p. 27-29, 1975. (Veterinária). Título em inglês: Prevalence and intensity of gastro-intestinal helminth infections in Ovis aries from the State of Bahia.Biblioteca(s): Embrapa Unidades Centrais. |
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10. | | PETERSEN, P.; SILVEIRA, L.; DIAS, E.; SANTOS, A. da S. dos; CURADO, F. F. Sementes ou grãos?: lutas para descontrução de uma falsa dicotomia. Agriculturas: experiências em agroecologia, Brasil, v. 10, n. 1, 2014. Corresponde ao v. 29, nº 1 da Revista Farming Matters.Tipo: Artigo de Divulgação na Mídia |
Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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13. | | SALES, I, dos S.; NOGUEIRA, C. H. de O.; SILVEIRA, L. S. da. Gota úrica visceral em coruja suindara (Tyto alba) de vida livre. Pesquisa Veterinária Brasileira, Brasília, DF, v. 35, n. 2, p. 169-172, fev. 2015.Biblioteca(s): Embrapa Unidades Centrais. |
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14. | | CARVALHO, A. P. M.; CARVALHO, E. C. Q.; DE NARDI, A. B.; SILVEIRA, L. S. Comparação de duas classificações histopatológicas com o padrão de imuno-marcação para KIT, a avaliação da proliferação celular e com a presença de mutações no c-KIT de mastocitomas cutâneos caninos. Pesquisa Veterinária Brasileira, Rio de Janeiro, v. 37, n. 4, p. 359-367, abril 2017. Título em inglês: Comparison of two histopathological classifications with pattern of immunostaining KIT, evaluation of cell proliferation and the presence of mutations in c-KIT canine cutaneous mast cell tumors.Biblioteca(s): Embrapa Unidades Centrais. |
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17. | | OLIVEIRA, M. D. de; BARBOSA, F. R.; CORTEZ, J. A.; SILVEIRA, L. C. P. Nuevas especies de triples (Thysanoptera: Thripidae) en mangos (Mangifera indica L.) en Brasil. In: SIMPÓSIO DE ENTOMOLOGIA, 1., REUNIÃO ANUAL DA SOCIEDADE DE ENTOMOLOGIA DA PARAÍBA, 3., 2007, Campina Grande. Entomologia e biodiversidade: anais. Campina Grande: Sociedade de Entomologia da Paraíba, 2007. p. 64.Tipo: Resumo em Anais de Congresso |
Biblioteca(s): Embrapa Semiárido. |
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18. | | OLIVEIRA, M. D. de; BARBOSA, F. R.; ALMEIDA-CORTEZ, J.; SILVEIRA, L. C. P. Espécies de Frankliniella (Thysanoptera, Thripidae): novos registros em mangueira (Mangifera indica) no Brasil. Ciência Rural, Santa Maria, v. 41, n. 10, p. 1709-1711, out. 2011.Biblioteca(s): Embrapa Arroz e Feijão. |
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Registros recuperados : 104 | |
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