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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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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
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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Registro original: |
Embrapa Café (CNPCa) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Soja. Para informações adicionais entre em contato com valeria.cardoso@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
29/10/1997 |
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Data da última atualização: |
02/06/2006 |
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Autoria: |
ALMEIDA, L. A.; KIIHL, R. A. S.; HARADA, A.; KAMIKOGA, M. K.; BOYE, R.; MIRANDA, L. C.; KASTER, M. |
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Afiliação: |
Embrapa Soja, Cx. Postal 231, CEP 86001-970 Londrina-PR. |
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Título: |
Avaliacao final 95/96 de cultivares e linhagens de soja para o Estado do Parana. |
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Ano de publicação: |
1997 |
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Fonte/Imprenta: |
In: REUNIAO DE PESQUISA DE SOJA DA REGIAO CENTRAL DO BRASIL, 18., 1996, Uberlandia. Ata e resumos. Uberlandia: UFU / DEAGRO, 1997. |
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Páginas: |
p.358-359. |
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Idioma: |
Português |
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Palavras-Chave: |
Avaliacao; Brasil; Cultivar; Cultivar line; Evaluation; Parana; Soybean; Variety. |
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Thesagro: |
Linhagem; Melhoramento; Soja. |
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Thesaurus NAL: |
Brazil; breeding. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00944naa a2200349 a 4500
001 1459553
005 2006-06-02
008 1997 bl uuuu u00u1 u #d
100 1 $aALMEIDA, L. A.
245 $aAvaliacao final 95/96 de cultivares e linhagens de soja para o Estado do Parana.
260 $c1997
300 $ap.358-359.
650 $aBrazil
650 $abreeding
650 $aLinhagem
650 $aMelhoramento
650 $aSoja
653 $aAvaliacao
653 $aBrasil
653 $aCultivar
653 $aCultivar line
653 $aEvaluation
653 $aParana
653 $aSoybean
653 $aVariety
700 1 $aKIIHL, R. A. S.
700 1 $aHARADA, A.
700 1 $aKAMIKOGA, M. K.
700 1 $aBOYE, R.
700 1 $aMIRANDA, L. C.
700 1 $aKASTER, M.
773 $tIn: REUNIAO DE PESQUISA DE SOJA DA REGIAO CENTRAL DO BRASIL, 18., 1996, Uberlandia. Ata e resumos. Uberlandia: UFU / DEAGRO, 1997.
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