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| 8. |  | VENSON, I.; ANDRADE, A. S. de; KLOCK, U.; MUÑIZ, G. I. B. de; NISGOSKI, S.; CARDOSO, G. da S. Influência da lavagem intermediária na deslignificação com oxigênio em polpa kraft. Ciência Florestal, Santa Maria, v. 25, n. 4, p. 991-999, out./dez., 2015| Biblioteca(s): Embrapa Florestas. |
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| 9. | | MUNIZ, G. I. B. de; IWAKIRI, S.; VIANA, L. C.; ANDRADE, M.; WEBER, C.; ALMEIDA, V. C. Production of plywood panels from Pinus taeda using veneers of differing densities and phenol-formaldehyde resin with high and low molecular weights. Cerne, Lavras, v. 19, n. 2, p. 315-321, abr./jun. 2013.| Biblioteca(s): Embrapa Florestas. |
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| 11. | | LIMA, R. A. de; LOPES, M. T. G.; BENTES, J. L. da S.; VALENTE, M. S. F.; PEREIRA, J. O.; MUNIZ, G. I. B. de. Diversidade e estrutura genética de Senna reticulata. Floresta, Curitiba, v. 45, n. 3, p. 507-514, jul./set. 2015.| Biblioteca(s): Embrapa Florestas. |
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| 12. | | BELINI, U. L.; FILHO, M. T.; CASTRO, V. R. de; MUNIZ, G. I. B. de; LASSO, P. R. O.; VAZ, C. M. P. Microtomografia de raios X (microCT) aplicada na caracterização anatômica da madeira de folhosa e de conífera. Floresta e Ambiente, Seropedica, v. 18, n. 1, p. 30-36, 2011.| Biblioteca(s): Embrapa Instrumentação. |
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| 13. | | BONFATTI JÚNIO, E. A.; LENGOWSKI, E. C.; TAKAHASHI, V. M.; ADUR, G. M.; SILVA, D. A. da; KLOCK, U.; ANDRADE, A. S. de; VERSON, I.; MUÑIZ, G. I. B. de. Briquetagem dos resíduos dos processamentos mecânico e químico de Pinus spp. Cadernos de Ciência & Tecnologia, v. 36, n. 3, e26522, set./dez. 2019. Título em inglês: Briquetting of waste from mechanical and chemical processes of Pinus spp.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 14. | | DIAS, I. A.; HORTA, R. P.; MATOS, M.; HELM, C. V.; MAGALHAES, W. L. E.; LIMA, E. A. de; SILVA, B. J. G. da; MUNIZ, G. I. B. de; CADEMARTORI, P. H. G. de. Exploring the antioxidant and antimicrobial properties of the water-soluble fraction derived from pyrolytic lignin separation in fast-pyrolysis bio-oil. Biomass Conversion and Biorefinery, p. 1-12, 2023. Online first.| Biblioteca(s): Embrapa Florestas. |
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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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