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| Registros recuperados : 12 | |
| 1. |  | SOUZA, B. M. de; FREITAS, M. L. M.; GEZAN, S. A.; ZANATTO, B.; AGUIAR, A. V. de. Genetic parameters, genotype-by-environment interaction, and genetic gains in Corymbia citriodora hook. In: SIMPOSIO INTERNACIONAL DE RECURSOS GENÉTICOS PARA LAS AMÉRICAS Y EL CARIBE, 11., 2017, Guadalajara. Resúmenes... Guadalajara: [s. n.], 2017. p. 172.| Biblioteca(s): Embrapa Florestas. |
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| 3. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARAES, C. T.; NODA, R. W.; SOUZA, J. C. de; PASTINA, M. M.; GUIMARAES, L. J. M. Seleção genômica para tolerância ao déficit hídrico em milho. In: CONGRESSO NACIONAL DE MILHO E SORGO, 31., 2016, Bento Gonçalves. Milho e sorgo: inovações, mercados e segurança alimentar: anais. Sete Lagoas: Associação Brasileira de Milho e Sorgo, 2016.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | 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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| 5. | | SOUZA, B. M. de; FREITAS, M. L. M.; SEBBENN, A. M.; GEZAN, S. A.; ZANATTO, B.; ZULIAN, D. F.; LOPES, M. T. G.; LONGUI, E. L.; GUERRINI, I. A.; AGUIAR, A. V. de. Genotype-by-environment interaction in Corymbia citriodora (Hook.) K.D. Hill, & L.A.S. Johnson progeny test in Luiz Antonio, Brazil. Forest Ecology and Management, v. 460, article 117855, Mar. 2020. 8 p.| Biblioteca(s): Embrapa Florestas. |
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| 6. | | 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. Genomic prediction in family bulks using different traits and cross-validations in pine. G3: Genes, Genomes, Genetics, v. 11, n. 9, p. 1-12, 2021.| Biblioteca(s): Embrapa Café. |
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| 7. | | MULLER, B. S. F.; ALMEIDA FILHO, J. E. de; LIMA, B. M.; GARCIA, C. C.; MISSIAGGIA, A.; AGUIAR, A. M.; TAKAHASHI, E.; KIRST, M.; GEZAN, S. A.; SILVA JUNIOR, O. B. da; NEVES, L. G.; GRATTAPAGLIA, D. Independent and Joint-GWAS for growth traits in Eucalyptus by assembling genome-wide data for 3373 individuals across four breeding populations. The New phytologist, v. 221, n. 2, p. 818-833, 2019. Na publicação: Orzenil B. Silva-Junior.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 8. | | FERREIRA, F. M.; CHAVES, S. F. S.; BHERING, L. L.; ALVES, R. S.; TAKAHASHI, E. K.; SOUSA, J. E.; RESENDE, M. D. V. de; LEITE, F. P.; GEZAN, S. A.; VIANA, J. M. S.; FERNANDES, S. B.; DIAS, K. O. G. A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. Forest Ecology and Management, v. 548, Article 121393, 2023. 10 p.| Biblioteca(s): Embrapa Café. |
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| 9. | | OLIVEIRA, I. C. M.; GUILHEN, J. H. S.; RIBEIRO, P. C. de O.; GEZAN, S. A.; SCHAFFERT, R. E.; SIMEONE, M. L. F.; DAMASCENO, C. M. B.; CARNEIRO, J. E. de S.; CARNEIRO, P. C. S.; PARRELLA, R. A. da C.; PASTINA, M. M. Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates. Field Crops Research, v. 257, 107929, 2020.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 10. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. Heredity, London, v. 121, n. 1, p. 24-37, 2018.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 11. | | PADUA, J. M. V.; DIAS, K. O. das G.; PASTINA, M. M.; SOUZA, J. C. de; QUEIROZ, V. A. V.; COSTA, R. V. da; SILVA, M. B. P. da; RIBEIRO, C. A. G.; GUIMARAES, C. T.; GEZAN, S. A.; GUIMARAES, L. J. M. A multi-environment trials diallel analysis provides insights on the inheritance of fumonisin contamination resistance in tropical maize. Euphytica, Dordrecht, v. 211, n. 3, p. 277-285, 2016| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 12. | | DIAS, K. O. das G.; GEZAN, S. A.; GUIMARAES, C. T.; MAGALHAES, J. V. de; GUIMARAES, P. E. de O.; CARNEIRO, N. P.; PORTUGAL, A. F.; BASTOS, E. A.; CARDOSO, M. J.; ANONI, C. de O.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. Estimating genotype X environment interaction for and genetic correlations among drought tolerance traits in maize via factor analytic multiplicative mixed models. Crop Science, Madison, v. 58, p. 72-83, Jan. 2018. Publicado online em 30 out. 2017.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 12 | |
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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
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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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