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| Registros recuperados : 48 | |
| 21. |  | 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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| 22. | | ALMEIDA FILHO, J. E. de; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JUNIOR, M. F. R. The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity, v. 117, p. 33-41, July 2016.| Biblioteca(s): Embrapa Florestas. |
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| 23. | | 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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| 24. | | 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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| 25. | | 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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| 26. | | 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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| 27. | | 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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| 28. | | 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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| 29. | | HOEKENGA, O. A.; BUCKLER, E.; MARON, L.; MAGALHAES, J. V. de; KIRST, M.; KRILL, A.; LYI, S. M.; ROSE, J.; THANNHAUSER, T.; KOCHIAN, L. Joint linkage-association analysis of aluminum tolerance in maize. In: INTERNATIONAL PLANT & ANIMAL GENOMES CONFERENCE, 15., 2007, San Diego, CA. [Proceedings...]. [S. l.: s.n.], 2007.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 30. | | HOEKENGA, O. A.; BUCKLER, E. S.; KIRST, M.; KRILL, A. M.; LYI, S. M.; MAGALHAES, J. V. de; MARON, L. G.; KOCHIAN, L. V. Joint linkage-association analysis of aluminum tolerance in maize. In: INTERNATIONAL SYMPOSIUM ON PLANT-SOIL INTERACTIONS AT LOW pH, 7., 2009, Guangzhou. Plant-soil interactions at low pH: nutriomic approach: proceedings. Guangzhou: South China University of Technology, 2009. p. 136-137.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 31. | | FERREYRA RAMOS, S. L.; DEQUIGIOVANNI, G.; SEBBENN, A. M.; LOPES, M. T. G.; KAGEYAMA, P. Y.; MACEDO, J. L. V. de; KIRST, M.; VEASEY, E. A. Spatial genetic structure, genetic diversity and pollen dispersal in a harvested population of Astrocaryum aculeatum in the Brazilian Amazon. BMC Genetics, 23 Apr. 2016.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| 34. | | AGUIAR, A. V. de; LOPES, M. T. G.; GAIOTTO, F. A.; BITTENCOURT, F.; DERVINIS, C.; MULLER, B. S. F.; SANTOS, R. F. dos; QUISEN, R. C.; KIRST, M. Transcriptome analysis of Euterpe edulis and identification of microsatellite markers. In: IUFRO GENOMICS & FOREST TREE GENETICS, 2016, Arcachon. Book of abstracts. [S.l.]: IUFRO, 2016. p. 90-91.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| 35. | | AGUIAR, A. V. de; LOPES, M. T. G.; GAIOTTO, F. A.; BITTENCOURT, F.; DERVINIS, C.; MULLER, B. S. F.; SANTOS, R. F. dos; QUISEN, R. C.; KIRST, M. Transcriptome analysis of Euterpe edulis and identification of microsatellite markers. In: IUFRO GENOMICS & FOREST TREE GENETICS, 2016, Arcachon. Book of abstracts. [S.l.]: IUFRO, 2016. p. 90-91.| Biblioteca(s): Embrapa Florestas. |
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| 36. | | 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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| 37. | | 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 Recursos Genéticos e Biotecnologia. |
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| 38. | | RESENDE JUNIOR, M. F. R.; MUÑOZ, P.; RESENDE, M. D. V. de; GARRICK, D. J.; FERNANDO, R. L.; DAVIS, J. M.; JOKELA, E. J.; MARTIN, T. A.; PETER, G. F.; KIRST, M. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) Genetics, v. 190, p. 1503-1510, April 2012.| Biblioteca(s): Embrapa Florestas. |
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| 39. | | LOPES, M. T. G.; GAIOTTO, F. A.; AGUIAR, A. V. de; FAHRENKROG, A.; BITTENCOURT, F.; DERVINIS, C.; MULLER, B. S. F.; SANTOS, R. F. dos; QUISEN, R. C.; KIRST, M. Next-generation transcriptome assembly of an Amazon palm (Euterpe precatoria). In: IUFRO GENOMICS & FOREST TREE GENETICS, 2016, Arcachon. Book of abstracts. [S.l.]: IUFRO, 2016. p. 90.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| 40. | | LOPES, M. T. G.; GAIOTTO, F. A.; AGUIAR, A. V. de; FAHRENKROG, A.; BITTENCOURT, F.; DERVINIS, C.; MULLER, B. S. F.; SANTOS, R. F. dos; QUISEN, R. C.; KIRST, M. Next-generation transcriptome assembly of an Amazon palm (Euterpe precatoria). In: IUFRO GENOMICS & FOREST TREE GENETICS, 2016, Arcachon. Book of abstracts. [S.l.]: IUFRO, 2016. p. 90.| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 48 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
08/11/2019 |
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Data da última atualização: |
08/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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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. |
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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. |
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Título: |
Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019. |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
BayesA; Genomic Prediction; Genotypic Value; GenPred; Oligogenic; Polygenic; Predição genòmica; RKHS; Shared Data Resources. |
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Thesagro: |
Genótipo. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
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001 2114084
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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.
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