| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Pantanal. |
|
Data corrente: |
11/11/2015 |
|
Data da última atualização: |
12/11/2015 |
|
Autoria: |
BERMEJO, J. V. D.; BAENA, S. N. (coord.). |
|
Afiliação: |
JUAN VICENTE DELGADO BERMEJO; SERGIO NOGALES BAENA. |
|
Título: |
Biodiversidad ovina Iberoamericana: caracterización y uso sustentable. |
|
Ano de publicação: |
2010 |
|
Fonte/Imprenta: |
Cordoba: Servicio de publicaciones, Universidad d Cordoba, 2010. |
|
Páginas: |
480 p. |
|
Idioma: |
Português |
|
Conteúdo: |
Sumário: Cap.1: Las razas ovinas ibéricas y su participación en la colonización de Iberoamérica; Cap. 2: Biodiversidad ovina en el centro de España: razas y usos; Cap. 3: Biodiversidad ovina en el noroeste de España; Cap. 4: Biodiversidad ovina en la España mediterránea e Islas Baleares; Cap. 5: Biodiversidad ovina em el sur de España e Islas Canarias; Cap. 6: Recursos genéticos ovinos locais de Portugal; Cap. 6: Recursos genéticos ovinos locais de Portugal; Cap. 7: Biodivesità ovina in Italia. Cap. 8: Recursos zoogenéticos ovinos em Uruguay. Cap. 9: Recursos genéticos ovinos de El Salvador; Cap. 10: El ovino criollo em Colombia, conservación, caracterización y evalación de la variabilidade genética; Cap. 11: Ovino Pelibuey cubano; Cap. 12: Estado da arte da conservação de ovinos no Brasil; Cap. 13: La cria de ovinos em Bolívia; Cap. 14: Diversidad y sistemas de cria de la espécie ovina em Venezuela. Cap. 15: Situación del sector ovino en Nicaragua. Cap 16: México: panorâmica de la ovinocultura nacional; Cap. 17: El ovino Pelibuey em el trópico mexicano; Cap. 18: Situación de la producción ovina em Paraguay; Cap. 19: Importancia del recurso ovino peruano em el desarrolho rural sostenible; Cap. 20: Recursos genéticos ovinos em Argentina; Cap. 21: Situación actual y perspectivas de los ovinos em Ecuador; Cap. 22: Razas criollas ovinas em los Estados Unidos. |
|
Palavras-Chave: |
Ovejas. |
|
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
|
Marc: |
LEADER 01826nam a2200145 a 4500
001 2028396
005 2015-11-12
008 2010 bl uuuu 00u1 u #d
100 1 $aBERMEJO, J. V. D.
245 $aBiodiversidad ovina Iberoamericana$bcaracterización y uso sustentable.
260 $aCordoba: Servicio de publicaciones, Universidad d Cordoba$c2010
300 $a480 p.
520 $aSumário: Cap.1: Las razas ovinas ibéricas y su participación en la colonización de Iberoamérica; Cap. 2: Biodiversidad ovina en el centro de España: razas y usos; Cap. 3: Biodiversidad ovina en el noroeste de España; Cap. 4: Biodiversidad ovina en la España mediterránea e Islas Baleares; Cap. 5: Biodiversidad ovina em el sur de España e Islas Canarias; Cap. 6: Recursos genéticos ovinos locais de Portugal; Cap. 6: Recursos genéticos ovinos locais de Portugal; Cap. 7: Biodivesità ovina in Italia. Cap. 8: Recursos zoogenéticos ovinos em Uruguay. Cap. 9: Recursos genéticos ovinos de El Salvador; Cap. 10: El ovino criollo em Colombia, conservación, caracterización y evalación de la variabilidade genética; Cap. 11: Ovino Pelibuey cubano; Cap. 12: Estado da arte da conservação de ovinos no Brasil; Cap. 13: La cria de ovinos em Bolívia; Cap. 14: Diversidad y sistemas de cria de la espécie ovina em Venezuela. Cap. 15: Situación del sector ovino en Nicaragua. Cap 16: México: panorâmica de la ovinocultura nacional; Cap. 17: El ovino Pelibuey em el trópico mexicano; Cap. 18: Situación de la producción ovina em Paraguay; Cap. 19: Importancia del recurso ovino peruano em el desarrolho rural sostenible; Cap. 20: Recursos genéticos ovinos em Argentina; Cap. 21: Situación actual y perspectivas de los ovinos em Ecuador; Cap. 22: Razas criollas ovinas em los Estados Unidos.
653 $aOvejas
700 1 $aBAENA, S. N.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Pantanal (CPAP) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
08/11/2019 |
|
Data da última atualização: |
08/11/2019 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
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. |
|
Afiliação: |
Janeo Eustáquio de Almeida Filho, Universidade Esatdual do Norte Fluminense e "Darcy Ribeiro"; João Filipi Rodrigues Guimarães, Futuragene Ltda; Fabyano Fonsceca e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Patricio Muñoz, University of Florida; Matias Kirst, University of Florida; Marcio Fernando Ribeiro de Resende Júnior, University of Florida. |
|
Título: |
Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. |
|
Ano de publicação: |
2019 |
|
Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019. |
|
Idioma: |
Inglês |
|
Conteúdo: |
The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits MenosThe genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and ... Mostrar Tudo |
|
Palavras-Chave: |
BayesA; Genomic Prediction; Genotypic Value; GenPred; Oligogenic; Polygenic; Predição genòmica; RKHS; Shared Data Resources. |
|
Thesagro: |
Genótipo. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
Marc: |
LEADER 02704naa a2200313 a 4500
001 2114084
005 2019-11-08
008 2019 bl uuuu u00u1 u #d
100 1 $aALMEIDA FILHO, J. E. de A.
245 $aGenomic prediction of additive and non-additive effects using genetic markers and pedigrees.$h[electronic resource]
260 $c2019
520 $aThe genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits
650 $aGenótipo
653 $aBayesA
653 $aGenomic Prediction
653 $aGenotypic Value
653 $aGenPred
653 $aOligogenic
653 $aPolygenic
653 $aPredição genòmica
653 $aRKHS
653 $aShared Data Resources
700 1 $aGUIMARÃES, J. F. R.
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aMUÑOZ, P.
700 1 $aKIRST, M.
700 1 $aRESENDE JÚNIOR, M. F. R. de
773 $tG3: Genes, Genomes, Genetics$gv. 9, p. 2739-2748, Aug. 2019.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
