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| Registros recuperados : 670 | |
| 581. |  | ROCHA, J. R. do A. S. de C.; MACHADO, J. C.; CARNEIRO, P. C. S.; CARNEIRO, J. da C.; RESENDE, M. D. V. de; PEREIRA, A. V.; CARNEIRO, J. E. de S. Elephant grass ecotypes for bioenergy production via direct combustion of biomass. Industrial Crops and Products, v. 95, p. 27-32, 2017.| Biblioteca(s): Embrapa Florestas; Embrapa Gado de Leite. |
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| 582. | | RESENDE, R. T.; CARNEIRO, A. de C. O.; FERREIRA, R. A. D. C.; KUKI, K. N.; TEIXEIRA, R. U.; ZAIDAN, U. R.; SANTOS, R. D.; LEITE, H. G.; RESENDE, M. D. V. de. Air-drying of eucalypts logs: Genetic variations along time and stem profile. Industrial Crops and Products, v 124, p. 316-324, Nov. 2018.| Biblioteca(s): Embrapa Florestas. |
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| 583. | | GLÓRIA, L. S.; CRUZ, C. D.; VIEIRA, R. A. M.; RESENDE, M. D. V. de; LOPES, P. S.; SIQUEIRA, O. H. G. B. D. de; SILVA, F. F. e. Accessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks. Livestock Science, v. 191, p. 91-96, Sept. 2016.| Biblioteca(s): Embrapa Florestas. |
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| 584. | | 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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| 585. | | MAIA, M. C. C.; RESENDE, M. D. V. de; OLIVEIRA, L. C. de; ALVES, R. M.; SILVA FILHO, J. L. da; ROCHA, M. de M.; CAVALCANTE, J. J. V.; RONCATTO, G. Análise genética de famílias de meios-irmãos de cupuaçuzeiro. Pesquisa Florestal Brasileira, Colombo, v. 31, n. 66, p. 123-130, abr./jun. 2011.| Biblioteca(s): Embrapa Acre; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Florestas; Embrapa Meio-Norte. |
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| 586. | | SIMEÃO, R. M.; RAPOSO, A.; VILELA, M. de M.; MARTINS, F. B.; RESENDE, M. D. V. de; BARRIOS, S. C. L.; MEIRELES, K. G. X.; VALLE, C. B. do; JANK, L.; SANTOS, M. F.; SOUSA, A. P. de. Melhoramento genético de Brachiaria ruziziensis Germain & Evrard (sin. Urochloa ruziziensis) autotetraploide: resultados do segundo ciclo de seleção intrapopulacional e estratégias para aumentar a eficiência da seleção. Campo Grande, MS: Embrapa Gado de Corte, 2022. (Embrapa Gado de Corte. Boletim de Pesquisa e Desenvolvimento, 50).| Biblioteca(s): Embrapa Gado de Corte. |
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| 587. | | PINTO JUNIOR, J. E.; SANTOS, P. E. T. dos; AGUIAR, A. V. de; KALIL FILHO, A. N.; PALUDZYSZYN FILHO, E.; STURION, J. A.; RESENDE, M. D. V. de; SOUSA, V. A. de. Melhoramento genético de espécies arbóreas na Embrapa Florestas: uma visão histórica. Colombo: Embrapa Florestas, 2013. 109 p. (Embrapa Florestas. Documentos, 259).| Biblioteca(s): Embrapa Florestas. |
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| 588. | | RESENDE, R. M. S.; VALLE, C. B. do; RESENDE, M. D. V. de; MEDEIROS, S. R. de; SILVA, A. S.; RAGALZI, C. de M.; JANK, L.; BARRIOS, S. C. L.; SANTOS, M. F. Melhoramento de Brachiaria ruziziensis Germain & Evrard (sin. Urochloa ruziziensis) autotetraploide: resultados da avaliação genética de subpopulações, progênies e indivíduos. Brasília, DF: Embrapa, 2016. 30 p. (Embrapa Gado de Corte. Boletim de Pesquisa e Desenvolvimento, 37).| Biblioteca(s): Embrapa Florestas; Embrapa Gado de Corte. |
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| 589. | | ALMEIDA FILHO, J. E.; TARDIN, F. D.; GUIMARAES, J. F. R.; RESENDE, M. D. V.; SILVA, F. F.; SIMEONE, M. L. F.; MENEZES, C. B. de; QUEIROZ, V. A. V. Multi-trait BLUP model indicates sorghum hybrids with genetic potential for agronomic and nutritional traits. Genetics and Molecular Research, Ribeirão Preto, v. 15, n. 1, p. 1-9, 2016.| Biblioteca(s): Embrapa Florestas; Embrapa Milho e Sorgo. |
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| 590. | | FERREIRA, F. M.; CHAVES, S. F. da S.; PEIXOTO, M. A.; ALVES, R. S.; COELHO, I. F.; RESENDE, M. D. V. de; SANTOS, G. A. dos; BHERING, L. L. Multi-trait multi-environment models for selecting high-performance and stable eucalyptus clones. Acta Scientiarum. Agronomy, v. 45, e61626, 2023. 9 p.| Biblioteca(s): Embrapa Café. |
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| 591. | | FERREIRA, F. M.; EVANGELISTA, J. S. P. C.; CHAVES, S. F. da S.; ALVES, R. S.; SILVA, D. B.; MALIKOUSKI, R. G.; RESENDE, M. D. V. de; BHERING, L. L.; SANTOS, G. A. Multivariate bayesian analysis for genetic evaluation and selection of eucalyptus in multiple environment trials. Bragantia, v. 81, e2922, 2022. 11 p.| Biblioteca(s): Embrapa Café. |
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| 592. | | ALVES, R. S.; TEODORO, P. E.; PEIXOTO, L. de A.; ROCHA, J. R. do A. S. de C.; SILVA, L. A.; LAVIOLA, B. G.; RESENDE, M. D. V. de; BHERING, L. L. Multiple-trait BLUP in longitudinal data analysis on Jatropha curcas breeding for bioenergy. Industrial Crops & Products, v.130, p. 558-561, 2019.| Biblioteca(s): Embrapa Agroenergia; Embrapa Florestas. |
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| 593. | | ALVES, R. S.; ROCHA, J. R. do A. de C.; TEODORO, P. E.; RESENDE, M. D. V. de; HENRIQUES, E. P.; SILVA, L. A.; CARNEIRO, P. C. S.; BHERING, L. L. Multiple-trait BLUP: a suitable strategy for genetic selection of Eucalyptus. Tree Genetics & Genomes, v. 14, n. 5, article 77, Oct. 2018. 8 p.| Biblioteca(s): Embrapa Florestas. |
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| 594. | | PEIXOTO, M. A.; EVANGELISTA, J. S. P. C.; COELHO, I. F; ALVES, R. A.; LAVIOLA, B. G.; SILVA, F. F. e; RESENDE, M. D. V. de; BHERING, L. L. Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy. PLOS ONE , v. 16, n. 3, e0247775, Mar. 2021. 16| Biblioteca(s): Embrapa Agroenergia; Embrapa Café. |
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| 595. | | PAIVA, J. T.; RESENDE, M. D. V. de; RESENDE, R. T.; OLIVEIRA, H. R.; SILVA, H. T.; CAETANO, G. C.; CALDERANO, A. A.; LOPES, P. S.; VIANA, J. M. S.; SILVA, F. F. A note on transgenerational epigenetics affecting egg quality traits in meat-type quail. British Poultry Science, v. 59, n. 6, p. 624-628, 2018. Short Communication.| Biblioteca(s): Embrapa Florestas. |
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| 596. | | BASTOS, I. T.; BARBOSA, M. H. P.; RESENDE, M. D. V. de; PEDROZO, C. A.; MELO, C. G.; PETERNELLI, L. A.; COSTA, P. M. de A.; XAVIER, C. V.; BAFFA, C. F. Correlation among predicted genotypic values and adaptability and stability estimates of sugarcane clones in a mixed models context. Scientia Agraria, Curitiba, v. 10, n. 2, p. 111-118, Mar./Abr. 2009.| Biblioteca(s): Embrapa Florestas. |
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| 597. | | CORRÊA, T. R.; PICOLI, E. A. de T.; SOUZA, G. A. de; CONDÉ, S. A.; SILVA, N. M.; LOPES-MATTOS, K. L. B.; RESENDE, M. D. V. de; ZAUZA, E. A. V.; ODA, S. Phenotypic markers in early selection for tolerance to dieback in Eucalyptus. Industrial Crops and Products, v. 107, p. 130-138, 2017.| Biblioteca(s): Embrapa Florestas. |
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| 598. | | GOMES JUNIOR, R. A.; SILVA, M. P. da; SANTOS, R. F. S. dos; PINA, A. J. de A.; QUARESMA, C. E.; CUNHA, R. N. V. da; LOPES, R.; RESENDE, M. D. V. de. Parâmetros genéticos e ganho na seleção em população de híbridos interespecíficos entre caiaué e dendê. 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.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| 599. | | GOMES JUNIOR, R. A.; SILVA, M. P. da; SANTOS, R. F. S. dos; PINA, A. J. de A.; QUARESMA, C. E.; CUNHA, R. N. V. da; LOPES, R.; RESENDE, M. D. V. de. Parâmetros genéticos e ganho na seleção em população de híbridos interespecíficos entre caiaué e dendê. 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 Amazônia Oriental; Embrapa Florestas. |
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| 600. | | SOARES, B. O.; JUHASZ, A. C. P.; PIMENTA, S.; RABELLO, H. de O.; RESENDE, M. D. V. de; COSTA, M. R.; NOBRE, D. A. C.; SOUZA, D. A. de. Pârametros genéticos da primeira produção de 88 famílias de meios irmãos de Jatropha curcas. In: CONGRESSO BRASILEIRO DE PESQUISA EM PINHÃO MANSO, 1., 2009, Brasília, DF. Pesquisa, desenvolvimento e inovação: tecnologia para biocombústiveis: anais. São Paulo: ABPPM; Brasília, DF: Embrapa Agroenergia, 2009.| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 670 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
20/01/2022 |
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Data da última atualização: |
20/01/2022 |
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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: |
RIOS, E. F.; ANDRADE, M. H. M. L.; RESENDE JR, M. F. R.; KIRST, M.; RESENDE, M. D. V. de; ALMEIDA FILHO, J. O. E. de; GEZAN, S. A.; MUNOZ, P. |
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Afiliação: |
ESTEBAN FERNANDO RIOS, UNIVERSITY OF FLORIDA; MARIO H M L ANDRADE, UNIVERSITY OF FLORIDA; MARCIO F R RESENDE JR, UNIVERSITY OF FLORIDA; MATIAS KIRST, UNIVERSITY OF FLORIDA; MARCOS DEON VILELA DE RESENDE, CNPCa; JANEO E DE ALMEIDA FILHO, BAYER CROP SCIENCE; SALVADOR A GEZAN, VSN INTERNATIONAL; PATRICIO MUNOZ, UNIVERSITY OF FLORIDA. |
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Título: |
Genomic prediction in family bulks using different traits and cross-validations in pine. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 11, n. 9, p. 1-12, 2021. |
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DOI: |
https://doi.org/10.1093/g3journal/jkab249 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5?20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations. MenosGenomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5?20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotype... Mostrar Tudo |
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Thesagro: |
Melhoramento Genético Vegetal; Reprodução Vegetal. |
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Thesaurus NAL: |
Genomics; Pineus; Statistical models. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230418/1/Genomic-prediction-in-family-bulks.pdf
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Marc: |
LEADER 02559naa a2200277 a 4500
001 2139221
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008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1093/g3journal/jkab249$2DOI
100 1 $aRIOS, E. F.
245 $aGenomic prediction in family bulks using different traits and cross-validations in pine.$h[electronic resource]
260 $c2021
520 $aGenomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5?20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.
650 $aGenomics
650 $aPineus
650 $aStatistical models
650 $aMelhoramento Genético Vegetal
650 $aReprodução Vegetal
700 1 $aANDRADE, M. H. M. L.
700 1 $aRESENDE JR, M. F. R.
700 1 $aKIRST, M.
700 1 $aRESENDE, M. D. V. de
700 1 $aALMEIDA FILHO, J. O. E. de
700 1 $aGEZAN, S. A.
700 1 $aMUNOZ, P.
773 $tG3: Genes, Genomes, Genetics$gv. 11, n. 9, p. 1-12, 2021.
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Embrapa Café (CNPCa) |
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