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| Registros recuperados : 30 | |
| 2. |  | NASCIMENTO, M.; SILVA, F. F. e; SAFADI, T.; NASCIMENTO, A. C. C.; FERREIRA, R. de P.; CRUZ, C. D. Abordagem bayesiana para avaliação da adaptabilidade e estabilidade de genótipos de alfafa. Pesquisa Agropecuária Brasileira, Brasília, v. 46, n. 1, p. 26-32, jan. 2011.| Biblioteca(s): Embrapa Pecuária Sudeste; Embrapa Unidades Centrais. |
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| 3. | | NASCIMENTO, M.; NASCIMENTO, A. C. C.; CIRILLO, M. A.; FERREIRA, A.; PETERNELLI, L. A.; FERREIRA, R. de P. Association between responses obtained using adaptability and stability methods in alfalfa. Semina: Ciências Agrárias, Londrina, v. 34, n. 6, p. 2545-2554, nov./dez. 2013.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 4. | | NASCIMENTO, N; FERREIRA, A.; NASCIMENTO, A. C. C.; SILVA, F. F. e; FERREIRA, R. de P.; CRUZ, C. D. Multiple centroid method to evaluate the adaptability of alfalfa genotypes. Revista Ceres, Viçosa, v. 62, n. 1, p. 030-036, jan/fev, 2015| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 7. | | OLIVEIRA, G. F.; MIRANDA, T. L. R.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; CAIXETA, E. T.; SILVA, L. de F.; ALKIMIM, E. R.; SILVA, F. L. da. Discrimination of varietal groups and hybrids of coffea canephora species using multivariate analysis. Revista Brasileira de Biometria, v. 39, n. 1, p. 194-201, 2021.| Biblioteca(s): Embrapa Café. |
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| 8. | | COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. Determination of optimal number of independent components in yield traits in rice. Scientia Agricola, v. 79, n. 6, p. 1-8, 2022.| Biblioteca(s): Embrapa Café. |
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| 9. | | SUELA, M. M.; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; MOMEN, M.; OLIVEIRA, A. C. B. de; CAIXETA, E. T.; MOROTA, G.; NASCIMENTO, M. Genome-wide association study for morphological, physiological, and productive traits in Coffea arabica using structural equation models. Tree Genetics & Genomes, v. 19, n. 3, 2023. 17 p.| Biblioteca(s): Embrapa Café. |
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| 13. | | BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; FONSECA, F. F. e; CRUZ, C. D.; BHERING, L. L.; FERREIRA, R. de P. Metodologia para análise de adaptabilidade e estabilidade por meio de regressão quantílica. Pesquisa Agropecuária Brasileira, v. 50, n. 4, p. 290-297, abr. 2015.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 14. | | BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F. e; CRUZ, C. D.; BHERING, L. L.; FERREIRA, R. de P. Metodologia para análise de adaptabilidade e estabilidade por meio de regressão quantílica. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 4, p. 267-290-297, abr. 2015. Título em inglês: Methodology for analysis of adaptability and stability using quantile regression.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 15. | | AZEVEDO, C. F.; CARVALHO, I. R.; NASCIMENTO, M.; SILVA, J. A. G. da; NASCIMENTO, A, C. C.; CRUZ, C. D.; HUTH, C.; ALMEIDA, H. C. F. de. Informative prior distribution applied to linseed for the estimation of genetic parameters using a small sample size. Pesquisa Agropecuária Brasileira, v. 57, e02793, 2022. Título em português: Distribuição a priori informativa aplicada à linhaça para estimação de parâmetros genéticos com uso de tamanho amostral reduzido.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 16. | | NASCIMENTO, M.; ROCHA, G. S. da; PINTO, D. S.; BARROSO, L. M. A.; NASCIMENTO, A. C. C.; FERREIRA, R. de P.; SILVA, F. F. e. Correlação de Spearman aplicada ao estudo de adaptabilidade e estabilidade em genótipos de alfafa. Investigacion Agrária, v. 15, n. 2, p. 83-90, 2013.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 17. | | COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. A comparison of regression methods based on dimensional reduction for genomic prediction. Genetics and Molecular Research, v. 20, n. 2, p. 1-15, 2021.| Biblioteca(s): Embrapa Café. |
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| 18. | | VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | OLIVEIRA, G. F.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; SANT'ANNA, I. de C.; ROMERO, J. V.; AZEVEDO, C. F.; BHERING, L. L.; CAIXETA, E. T. Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. Plos One, v. 16, n. 1, e0243666, 2021.| Biblioteca(s): Embrapa Café. |
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| 20. | | CARVALHO, V. P.; SANT'ANNA, I. C.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; CRUZ, C. D.; ARBEX, W. A.; OLIVEIRA, F. C.; SILVA, F. F. Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity. Genetics and Molecular Research, v. 17, n. 4, gmr18122, 2018. 10 p.| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 30 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
03/01/2024 |
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Data da última atualização: |
03/01/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
AZEVEDO, C. F.; FERRÃO, L. F. V.; BENEVENUTO, J.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; MUNOZ, P. R. |
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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. |
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Título: |
Using visual scores for genomic prediction of complex traits in breeding programs. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 137, n. 1, 2024. |
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Páginas: |
16 p. |
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DOI: |
https://doi.org/10.1007/s00122-023-04512-w |
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Idioma: |
Inglês |
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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 |
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Thesaurus NAL: |
Animal breeding; Bayesian theory; Genome; Inheritance (genetics); Phenotype; Plant breeding. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1160409/1/Using-visual-scores-for-genomic-prediction.pdf
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Marc: |
LEADER 02817naa a2200289 a 4500
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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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