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| Registros recuperados : 28 | |
| 12. |  | MACHADO, G. M. E.; REGAZZI, A. J.; VIANA, J. M. S.; CRUZ, C. D.; GRANATE, M. J. Estimação de parâmetros genéticos de uma população amazônica de cupuaçuzeiro (Theobroma grandiflorum (Wild ex Spreng) Schum). Revista Ceres, Viçosa, v. 49, n. 281, p. 13-27, 2002.| Biblioteca(s): Embrapa Florestas. |
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| 13. | | VALENTE, M. S.; VIANA, J. M. S.; RESENDE, M. D. V. de; SILVA, F. F. e; LOPES, M. T. G. Seleção genômica para melhoramento vegetal com diferentes estruturas populacionais. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 11, p. 1857-1867, nov. 2016. Título em inglês: Genomic selection for plant breeding with different population structures.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 14. | | MARIGUELE, K. H.; RESENDE, M. D. V. de; VIANA, J. M. S.; SILVA, F. F. e; SILVA, P. S. L. de; KNOP, F. de C. Métodos de análise de dados longitudinais para o melhoramento genético da pinha. Pesquisa Agropecuária Brasileira, Brasília, v. 46, n. 12, p. 1657-1664, dez. 2011.| Biblioteca(s): Embrapa Florestas; Embrapa Unidades Centrais. |
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| 16. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F.; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; OLIVEIRA, E. J. de. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program. Genetics and Molecular Research, v. 15, n. 4, gmr.15048838, Oct. 2016.| Biblioteca(s): Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
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| 17. | | LIMA L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; SUELA, M. M.; NASCIMENTO, M.; VIANA, J. M. S. New insights into genomic selection through population-based non-parametric prediction methods. Scientia Agricicola, v. 76, n. 4, p. 290-298, July/Aug. 2019.| Biblioteca(s): Embrapa Florestas. |
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| 18. | | VIANA, J. M. S.; DELIMA, R. O.; FARIA, V. R.; MUNDIM, G. B.; RESENDE, M. D. V. de; SILVA, F. F. e. Relevance of pedigree, historical data, dominance, and data unbalance for selection efficiency. Agronomy Journal, v. 104, n. 3, p. 722-728, 2012.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | 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.| Biblioteca(s): Embrapa Florestas. |
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| 20. | | LIMA, L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; OLIVEIRA, E. J. de. Triple categorical regression for genomic selection: application to cassava breeding. Scientia Agricola, v. 76, n. 5, p. 368-375, Sept./Oct. 2019.| Biblioteca(s): Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
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| Registros recuperados : 28 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
25/07/2018 |
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Data da última atualização: |
25/07/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
SILVA, F. F.; JEREZ, E. A. Z.; RESENDE, M. D. V. de; VIANA, J. M. S.; AZEVEDO, C. F.; LOPES, P. S.; NASCIMENTO, M.; LIMA, R. O. de; GUIMARÃES, S. E. F. |
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Afiliação: |
Fabyano Fonseca Silva, UFV; Elcer Albenis Zamora Jerez, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; José Marcelo Soriano Viana, UFV; Camila Ferreira Azevedo, UFV; Paulo Sávio Lopes, UFV; Moysés Nascimento, UFV; Rodrigo Oliveira de Lima, UFV; Simone Eliza Facioni Guimarães, UFV. |
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Título: |
Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Journal of Applied Animal Research, v. 46, n. 1, p. 873-878, 2018. |
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DOI: |
10.1080/09712119.2017.1415903 |
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Idioma: |
Inglês |
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Conteúdo: |
We combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ?LALDA?) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression ? BRR; Bayes A ? BA; Bayes B ? BB; Bayes C ? BC and Bayesian LASSO ? BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets. |
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Palavras-Chave: |
x. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/180314/1/2018-M.Deon-JAAQR-Bayesian.pdf
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Marc: |
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245 $aBayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding.$h[electronic resource]
260 $c2018
520 $aWe combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ?LALDA?) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression ? BRR; Bayes A ? BA; Bayes B ? BB; Bayes C ? BC and Bayesian LASSO ? BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets.
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773 $tJournal of Applied Animal Research$gv. 46, n. 1, p. 873-878, 2018.
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