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Registros recuperados : 25 | |
2. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | MUNOZ, P.; RESENDE JUNIOR, M.; RESENDE, M. D. V. de; GEZAN, S.; KIRST, M.; PETER, G. The re-discovery of the dominance variation by using the observed relationship matrix and itis implications in breeding. In: INTERNATIONAL CONFERENCE ON QUANTITATIVE GENETICS, 4., 2012, Edinburgh. Understanding Variation in Complex Traits. . [S.l.: s.n], 2012. Poster abstracts. P-367. Biblioteca(s): Embrapa Florestas. |
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3. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | BETTINI, S. H. P.; JOSEFOVICH, M. P. P. de M.; MUÑOZ, P. A. R.; LOTTI, C.; MATTOSO, L. H. C. Effect of lubricant on mechanical and rheological properties of compatibilized PP/sawdust composites. Carbohydrate Polymers, Barking, v. 94, n. 2, p. 800-806, 2013. Biblioteca(s): Embrapa Instrumentação. |
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4. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | RIOS, E.; RESENDE, M.; KIRST, M.; RESENDE, M. D. V. de; ALMEIDA FILHO, J. E. de; MUNOZ, P. Predictive ability of Genomic Estimated Family Values (GEFV). In: PLANT & ANIMAL GENOME CONFERENCE, 24., 2016, San Diego. [Abstracts...]. San Diego: [s.n.], 2016. Pôster P1186. Biblioteca(s): Embrapa Florestas. |
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5. | ![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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6. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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7. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | FIGUEIREDO, U. J. de; BERCHEMBROCK, Y. V.; VALLE, C. B. do; BARRIOS, S. C. L.; QUESENBERRY, K. H.; MUÑOZ, P. R.; NUNES, J. A. R. Evaluating early selection in perennial tropical forages. Crop Breeding and Applied Biotechnology, v. 19, n. 3, p. 291-299, 2019. Biblioteca(s): Embrapa Gado de Corte. |
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8. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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9. | ![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. Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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10. | ![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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11. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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12. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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13. | ![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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14. | ![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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15. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | AZEVEDO, C. F.; FERRÃO, L. F. V.; BENEVENUTO, J.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; MUNOZ, P. R. Using visual scores for genomic prediction of complex traits in breeding programs. Theoretical and Applied Genetics, v. 137, n. 1, 2024. 16 p. Biblioteca(s): Embrapa Café. |
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16. | ![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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17. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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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18. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | FERRAO, M. A. G.; FONSECA, A. F. A. da; VOLPI, P. S.; SOUZA, L. C. de; COMÉRIO, M.; VERDIN FILHO, A. C.; RIVA-SOUZA, E. M.; MUNOZ, P. R.; FERRÃO, R. G.; FERRÃO, L. F. V. Genomic-assisted breeding for climate-smart coffee. The Plant Genome, e20321, 2023. 19 p. Biblioteca(s): Embrapa Café. |
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19. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | BISWAS, A.; ANDRADE, M. H. M. L.; ACHARYA, J. P.; SOUZA, C. L. de; LOPEZ, Y.; ASSIS, G. M. L. de; SHIRBHATE, S.; SINGH, A.; MUNOZ, P.; RIOS, E. F. Phenomics-assisted selection for herbage accumulation in alfalfa (Medicago sativa L.). Frontiers in Plant Science, v. 12, 756768, Dec. 2021. Biblioteca(s): Embrapa Acre. |
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20. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | 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. Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019. Biblioteca(s): Embrapa Florestas. |
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Registros recuperados : 25 | |
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Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
20/01/2022 |
Data da última atualização: |
20/01/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
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. |
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. |
Título: |
Genomic prediction in family bulks using different traits and cross-validations in pine. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 11, n. 9, p. 1-12, 2021. |
DOI: |
https://doi.org/10.1093/g3journal/jkab249 |
Idioma: |
Inglês |
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 |
Thesagro: |
Melhoramento Genético Vegetal; Reprodução Vegetal. |
Thesaurus NAL: |
Genomics; Pineus; Statistical models. |
Categoria do assunto: |
-- |
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 005 2022-01-20 008 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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