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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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Biblioteca |
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| Registros recuperados : 24 | |
| 1. |  | MUNOZ, P.; RESENDE JUNIOR, M.; RESENDE, M. D. V. de; GEZAN, S.; KIRST, M.; PETER, G. The re-discovery of the dominance variation by using the observed relationship matrix and itis implications in breeding. In: INTERNATIONAL CONFERENCE ON QUANTITATIVE GENETICS, 4., 2012, Edinburgh. Understanding Variation in Complex Traits. . [S.l.: s.n], 2012. Poster abstracts. P-367.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 2. | | RIOS, E.; RESENDE, M.; KIRST, M.; RESENDE, M. D. V. de; ALMEIDA FILHO, J. E. de; MUNOZ, P. Predictive ability of Genomic Estimated Family Values (GEFV). In: PLANT & ANIMAL GENOME CONFERENCE, 24., 2016, San Diego. [Abstracts...]. San Diego: [s.n.], 2016. Pôster P1186.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 3. | | BETTINI, S. H. P.; JOSEFOVICH, M. P. P. de M.; MUÑOZ, P. A. R.; LOTTI, C.; MATTOSO, L. H. C. Effect of lubricant on mechanical and rheological properties of compatibilized PP/sawdust composites. Carbohydrate Polymers, Barking, v. 94, n. 2, p. 800-806, 2013.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Instrumentação. |
|  |
| 4. | | RESENDE JUNIOR, M. F. R.; MUÑOZ, P.; ACOSTA, J. J.; PETER, G. F.; DAVIS, J. M.; GRATTAPAGLIA, D.; RESENDE, M. D. V. de; KIRST, M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytologist, v. 193, p. 617-624, 2012.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| 5. | | ALMEIDA FILHO, J. E. de; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JUNIOR, M. F. R. The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity, v. 117, p. 33-41, July 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 6. | | GUIMARÃES, J. F. R.; ALMEIDA FILHO, J. E.; RESENDE JÚNIOR, M. F.; RESENDE, M. D. V. de; SILVA, F. F. e; MUÑOZ, P.; KIRST, M. Predictive ability behavior across sites after discard of SNPS with unstable effects. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: SBMP: UFG, 2015. Resumo.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 7. | | RESENDE JUNIOR, M.; RESENDE, M. D. V. de; MUNOZ, P. R.; TAKAHASHI, E. K.; PETROLI, C.; SANSALONI, C.; KIRST, M.; GRATTAPAGLIA, D. Increase in efficiency of genomic selection sing epistatic interactions and detection of candidate genes for rust resistance in Eucalyptus. In: INTERNATIONAL PLANT & ANIMAL GENOME, 21., 2013, San Diego. Abstracts... Jersey City: Scherago International, 2013. W287.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 8. | | RESENDE JUNIOR, M.; RESENDE, M. D. V. de; MUNOZ, P. R.; TAKAHASHI, E. K.; PETROLI, C.; SANSALONI, C.; KIRST, M.; GRATTAPAGLIA, D. Increase in efficiency of genomic selection sing epistatic interactions and detection of candidate genes for rust resistance in Eucalyptus. In: INTERNATIONAL PLANT & ANIMAL GENOME, 21., 2013, San Diego. Abstracts... Jersey City: Scherago International, 2013. W287.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 9. | | FIGUEIREDO, U. J. de; BERCHEMBROCK, Y. V.; VALLE, C. B. do; BARRIOS, S. C. L.; QUESENBERRY, K. H.; MUÑOZ, P. R.; NUNES, J. A. R. Evaluating early selection in perennial tropical forages. Crop Breeding and Applied Biotechnology, v. 19, n. 3, p. 291-299, 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 10. | | ALMEIDA FILHO, J. E. de A.; RODRIGUES, J. F. G.; SILVA, F. F. e; RESENDE, M. D. V. de; RESENDE JÚNIOR, M.; MUÑOZ, P.; KIRST, M. Genomic prediction of assitive and non-additive effects using genetic markers and pedigrees in pines breeding. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: SBMP: UFG, 2015. Resumo.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 11. | | MÜLLER, B. S. F.; NEVES, L. G.; RESENDE JÚNIOR, M. F. R.; MUÑOZ, P. R.; KIRST, M.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; GRATTAPAGLIA, D. Genomic selection for growth traits in Eucalyptus benthamii and E. pellita populations using a genome-wide Eucalyptus 60K SNPs chip. In: IUFRO TREE BIOTECHNOLOGY CONFERENCE, 2015, Florence. Forests: the importance to the planet and society. [S.l.]: IBBR: ICCOM, 2015.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 12. | | MÜLLER, B. S. F.; NEVES, L. G.; RESENDE JÚNIOR, M. F. R.; MUÑOZ, P. R.; KIRST, M.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; GRATTAPAGLIA, D. Genomic selection for growth traits in Eucalyptus benthamii and E. pellita populations using a genome-wide Eucalyptus 60K SNPs chip. In: IUFRO TREE BIOTECHNOLOGY CONFERENCE, 2015, Florence. Forests: the importance to the planet and society. [S.l.]: IBBR: ICCOM, 2015. Pen-drive.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 13. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. Ridge, Lasso and Bayesian additive dominance genomic models. BMC Genetics, v. 16, art. 105, Aug. 2015. 13 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 14. | | MUÑOZ, P. R.; RESENDE JUNIOR, M. F. R.; GEZAN, S. A.; RESENDE, M. D. V. de; CAMPOS, G. de los; KIRST, M.; HUBER, D.; PETER, G. F. Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics, v. 198, p. 1759-1768, Dec. 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 15. | | AZEVEDO, C. F.; FERRÃO, L. F. V.; BENEVENUTO, J.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; MUNOZ, P. R. Using visual scores for genomic prediction of complex traits in breeding programs. Theoretical and Applied Genetics, v. 137, n. 1, 2024. 16 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Café. |
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| 16. | | RESENDE JUNIOR, M. F. R.; MUÑOZ, P.; RESENDE, M. D. V. de; GARRICK, D. J.; FERNANDO, R. L.; DAVIS, J. M.; JOKELA, E. J.; MARTIN, T. A.; PETER, G. F.; KIRST, M. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) Genetics, v. 190, p. 1503-1510, April 2012.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| 17. | | BISWAS, A.; ANDRADE, M. H. M. L.; ACHARYA, J. P.; SOUZA, C. L. de; LOPEZ, Y.; ASSIS, G. M. L. de; SHIRBHATE, S.; SINGH, A.; MUNOZ, P.; RIOS, E. F. Phenomics-assisted selection for herbage accumulation in alfalfa (Medicago sativa L.). Frontiers in Plant Science, v. 12, 756768, Dec. 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Acre. |
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| 18. | | FERRAO, M. A. G.; FONSECA, A. F. A. da; VOLPI, P. S.; SOUZA, L. C. de; COMÉRIO, M.; VERDIN FILHO, A. C.; RIVA-SOUZA, E. M.; MUNOZ, P. R.; FERRÃO, R. G.; FERRÃO, L. F. V. Genomic-assisted breeding for climate-smart coffee. The Plant Genome, e20321, 2023. 19 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Café. |
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| 19. | | ALMEIDA FILHO, J. E. de A.; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JÚNIOR, M. F. R. de. Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas. |
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| 20. | | MÜLLER, B. S. F.; NEVES, L. G.; ALMEIDA FILHO, J. E. de; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. R.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; KIRST, M.; GRATTAPAGLIA, D. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics, v. 18, article 524, 2017. 17 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 24 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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