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| Registros recuperados : 36 | |
| 3. |  | 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.| Biblioteca(s): Embrapa Florestas. |
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| 9. | | ROSADO, A. M.; ROSADO, T. B.; RESENDE JÚNIOR, M. F. R.; BHERING, L. L.; CRUZ, C. D. Ganhos genéticos preditos por diferentes métodos de seleção em progênies de Eucalyptus urophylla Pesquisa Agropecuária Brasileira, Brasília, DF, v. 44, n. 12, p. 1653-1659, dez. 2009 Título em inglês: Predicted genetic gains by various selection methods in Eucalyptus urophylla progenies.| Biblioteca(s): Embrapa Agroenergia; Embrapa Unidades Centrais. |
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| 10. | | RESENDE JUNIOR, M. F. R.; ALVES, A. A.; BARRERA SÁNCHES, C. F.; RESENDE, M. D. V. de; CRUZ, C. D. Seleção genômica ampla. In: CRUZ, C. D.; SALGADO, C. C.; BHERING, L. L. (Ed.). Genômica aplicada. Viçosa, MG: Suprema, 2013. p. 375-424.| Biblioteca(s): Embrapa Agroenergia; Embrapa Florestas. |
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| 11. | | 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.| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| 12. | | BHERING, L. L.; CRUZ, C. D.; VASCONCELOS, E. S. de; RESENDE JUNIOR, M. F. R. de; BARROS, W. S.; ROSADO, T. B. Efficiency of the multilocus analysis for the construction of genetic maps. Crop Breeding and Applied Biotechnology, Londrina, v. 9, n. 4, p. 308-312, Dec. 2009.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Agroenergia. |
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| 13. | | 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.| Biblioteca(s): Embrapa Florestas. |
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| 14. | | 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.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 15. | | 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.| Biblioteca(s): Embrapa Florestas. |
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| 16. | | 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.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 17. | | 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.| Biblioteca(s): Embrapa Florestas. |
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| 18. | | RESENDE JUNIOR, M.; DELL VALLE, P. R. M.; RESENDE, M. D. V. de; GARRICK, D. J.; FERNANDO, R.; DAVIS, J. M.; PETER, G.; KIRST, M. Improvement of genomic selection using a ridge regression approach with selected markers. In: INTERNATIONAL CONFERENCE ON QUANTITATIVE GENETICS, 4., 2012, Edinburgh. Understanding Variation in Complex Traits. . [S.l.: s.n], 2012. Poster abstracts. P-199.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | 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.| Biblioteca(s): Embrapa Florestas. |
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| 20. | | SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; SAKIYAMA, N. S.; RESENDE JÚNIOR, M. F. R. de; ZAMBOLIM, L. Population structure and genetic diversity of coffee progenies derived from Catuaí and Híbrido de Timor revealed by genome-wide SNP marker. Tree Genetics & Genomes, v. 13, n. 6, Dec. 2017.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 36 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
24/08/2015 |
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Data da última atualização: |
25/02/2016 |
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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.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. |
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Afiliação: |
Camila Ferreira Azevedo, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, UFV; José Marcelo Soriano Viana, UFV; Magno Sávio Ferreira Valente, UFV; Márcio Fernando Ribeiro Resende Jr, Florida Innovation Hub; Patricio Muñoz, University of Florida. |
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Título: |
Ridge, Lasso and Bayesian additive dominance genomic models. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
BMC Genetics, v. 16, art. 105, Aug. 2015. 13 p. |
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DOI: |
10.1186/s12863-015-0264-2 |
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Idioma: |
Inglês |
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Conteúdo: |
Background: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models. MenosBackground: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS ... Mostrar Tudo |
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Palavras-Chave: |
Bayesian methods; Dominance genomic models; Genética quantitativa; Lasso methods; Melhoramento genético; Modelo Bayesiano; Selection accuracy. |
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Thesagro: |
Parâmetro Genético. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/128510/1/2015-API-Deon-Ridge.pdf
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Marc: |
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024 7 $a10.1186/s12863-015-0264-2$2DOI
100 1 $aAZEVEDO, C. F.
245 $aRidge, Lasso and Bayesian additive dominance genomic models.$h[electronic resource]
260 $c2015
520 $aBackground: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.
650 $aParâmetro Genético
653 $aBayesian methods
653 $aDominance genomic models
653 $aGenética quantitativa
653 $aLasso methods
653 $aMelhoramento genético
653 $aModelo Bayesiano
653 $aSelection accuracy
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. F. e
700 1 $aVIANA, J. M. S.
700 1 $aVALENTE, M. S. F.
700 1 $aRESENDE JUNIOR, M. F. R.
700 1 $aMUÑOZ, P.
773 $tBMC Genetics$gv. 16, art. 105, Aug. 2015. 13 p.
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