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Registros recuperados : 31 | |
7. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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9. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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10. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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11. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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12. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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14. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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15. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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16. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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17. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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. Biblioteca(s): Embrapa Florestas. |
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18. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | GRATTAPAGLIA, D.; SANSALONI, C.; PETROLI, C.; RESENDE JÚNIOR, M. F.; FARIA, D.; MISSIAGGIA, A. A.; TAKAHASHI. E. K.; ZAMPROGNO, K.; KILIAN, A.; RESENDE, M. D. V. de. Realized accuracies of Genomic Selection for volume growth in tropical Eucalyptus: marker assisted selection coming to reality in forest trees. In: CONGRESSO BRASILEIRO DE GENÉTICA, 56., 2010, Guarujá. Resumos... [Curitiba]: UFPR, 2010. p. 192. Biblioteca(s): Embrapa Florestas. |
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19. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | RESENDE, M. D. V. de; RESENDE JUNIOR, M. F. R.; AGUIAR, A. M.; ABAD, J. I. M.; MISSIAGGIA, A. A.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Computação da Seleção Genômica Ampla (GWS). Colombo: Embrapa Florestas, 2010. CD-ROM. (Embrapa Florestas. Documentos, 210). Biblioteca(s): Embrapa Florestas. |
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Registros recuperados : 31 | |
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Registro Completo
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
24/08/2015 |
Data da última atualização: |
25/02/2016 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
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. |
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. |
Título: |
Ridge, Lasso and Bayesian additive dominance genomic models. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
BMC Genetics, v. 16, art. 105, Aug. 2015. 13 p. |
DOI: |
10.1186/s12863-015-0264-2 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Bayesian methods; Dominance genomic models; Genética quantitativa; Lasso methods; Melhoramento genético; Modelo Bayesiano; Selection accuracy. |
Thesagro: |
Parâmetro Genético. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/128510/1/2015-API-Deon-Ridge.pdf
|
Marc: |
LEADER 02783naa a2200301 a 4500 001 2022575 005 2016-02-25 008 2015 bl uuuu u00u1 u #d 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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