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Registros recuperados : 42 | |
6. | | SAGRILO, E.; VIDIGAL FILHO, P. S.; PEQUENO, M. G.; VIDIGAL, M. C. G.; KVITSCHAL, M. V. Dry matter production and distribution in three cassava (Manihot esculenta Crantz) cultivars during the second vegetative plant cycle. Brazilian Archives of Biology and Technology, Curitiba, v. 51, n. 6, p. 1079-1087, nov./dec. 2008. Biblioteca(s): Embrapa Meio-Norte. |
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7. | | ROYER, M. R.; GONÇALVES-VIDIGAL, M. C.; SCAPIM, C. A.; VIDIGAL FILHO, P. S.; TERADA, Y. Outcrossing in common bean. Crop Breeding and Applied Biotechnology, Londrina, v. 2, n. 1, p. 49-54, Mar. 2002. Biblioteca(s): Embrapa Arroz e Feijão. |
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8. | | PEQUENO, M. G.; VIDIGAL FILHO, P. S.; ALBRECHT, S. C.; MAIA, R.; LIMA, R. S.; SIMON, G. A.; SAGDILO, E. Avaliacao de cultivares e clones de mandioca em araruna, nordeste do Estado do Parana Sao Pedro, SP: Centro de Raizes Tropicais/UNESP/SBM, 1996. 1p. In: CONGRESSO LATINO AMERICANO DE RAIZES TROPICAIS, 1, E CONGRESSO BRASILEIRO DE MANDIOCA, 9, 1996, Sao Pedro, SP. Programa e Resumos... Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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12. | | THOMAZELLA, C.; GONÇALVES-VIDIGAL, M. C.; VIDIGAL FILHO, P. S.; NUNES, W. M. de C.; VIDA, J. B. Characterization of Colletotrichum lindemuthianum races in Paraná state, Brazil. Crop Breeding and Applied Biotechnology, Londrina, v. 2, n. 1, p. 55-60, Mar. 2002. Biblioteca(s): Embrapa Arroz e Feijão. |
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20. | | MATOS, F. M. de; MEIRELLES, W. F.; NEGRI, B. F.; MENDES, F. F.; GUIMARAES, L. J. M.; PARENTONI, S. N.; VIDIGAL FILHO, P. S.; SOUSA, S. M. de. Análise da diversidade do sistema radicular de linhagens e híbridos de milho cultivados em solução nutritiva em diferentes níveis de fósforo. In: CONGRESSO NACIONAL DE MILHO E SORGO, 29., 2012, Águas de Lindóia. Diversidade e inovações na era dos transgênicos: resumos expandidos. Campinas: Instituto Agronômico; Sete Lagoas: Associação Brasileira de Milho e Sorgo, 2012. p. 3096-3101. 1 CD-ROM. Biblioteca(s): Embrapa Milho e Sorgo. |
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Registros recuperados : 42 | |
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Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
03/01/2024 |
Data da última atualização: |
03/01/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
AZEVEDO, C. F.; FERRÃO, L. F. V.; BENEVENUTO, J.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; MUNOZ, P. R. |
Afiliação: |
CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORID; JULIANA BENEVENUTO, UNIVERSITY OF FLORID; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSES NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; PATRICIO R. MUNOZ, UNIVERSITY OF FLORID. |
Título: |
Using visual scores for genomic prediction of complex traits in breeding programs. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 137, n. 1, 2024. |
Páginas: |
16 p. |
DOI: |
https://doi.org/10.1007/s00122-023-04512-w |
Idioma: |
Inglês |
Conteúdo: |
An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping. MenosAn approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and ... Mostrar Tudo |
Thesaurus NAL: |
Animal breeding; Bayesian theory; Genome; Inheritance (genetics); Phenotype; Plant breeding. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1160409/1/Using-visual-scores-for-genomic-prediction.pdf
|
Marc: |
LEADER 02817naa a2200289 a 4500 001 2160409 005 2024-01-03 008 2024 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1007/s00122-023-04512-w$2DOI 100 1 $aAZEVEDO, C. F. 245 $aUsing visual scores for genomic prediction of complex traits in breeding programs.$h[electronic resource] 260 $c2024 300 $a16 p. 520 $aAn approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping. 650 $aAnimal breeding 650 $aBayesian theory 650 $aGenome 650 $aInheritance (genetics) 650 $aPhenotype 650 $aPlant breeding 700 1 $aFERRÃO, L. F. V. 700 1 $aBENEVENUTO, J. 700 1 $aRESENDE, M. D. V. de 700 1 $aNASCIMENTO, M. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aMUNOZ, P. R. 773 $tTheoretical and Applied Genetics$gv. 137, n. 1, 2024.
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