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| Registros recuperados : 48 | |
| 8. |  | NEVES, L. G.; PAPPAS JUNIOR, G. J.; PAQUALI, G.; KIRST, M.; GRATTAPAGLIA, D. Advancing to a high density gene-rich map based on single feature polymorphisms in tropical eucalyptus. In: INTERNATIONAL PLANT & ANIMAL GENOMES CONFERENCE, 17., 2009, San Diego, CA. [Proceedings...]. [S. l.: s.n.], 2009. W185| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 9. | | BRONDANI, R. P. V.; GAIOTTO, F. A.; MISSIAGGIA, A. A.; KIRST, M.; GRIBELS, R.; GRATTAPAGLIA, D. Microsatellite markers for Ceiba pentandra (Bombacaceae), an endangered tree species of the amazon forest Molecular Ecology Notes, v.3, p.177-179, 2003.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 10. | | 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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| 13. | | GRATTAPAGLIA, D.; NOVAES, E.; PAPPAS, G. J.; PASQUALI, G.; KIRST, M. Single feature polymorphism discovery and validation in Eucalyptus by pseudo-testcross inheritance and mapping. In: THE INTERNATIONAL CONFERENCE ON THE STATUS OF PLANT & ANIMAL GENOME RESEARCH, 16., 2008, San Diego. Final abstracts guide. San Diego, 2008, W172.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 16. | | PROSDOCIMI, P; BITTENCOURT, D.; SILVA, F. R. da; KIRST, M.; MOTA, P. C.; RECH, E. L. Spinning gland transcriptomics from two main Clades of Spiders (Order: Araneae) - insights on their molecular, anatomical and behavioral evolution. Plos One, San Francisco, v. 6, n. 6, p. 1-15, June 2011.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Amazônia Ocidental; Embrapa Recursos Genéticos e Biotecnologia. |
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| 17. | | MISSIAGGIA, A. A.; KIRST, M.; SILVEIRA, F. Q.; GAIOTTO, F. A.; BRONDANI, R. P. V.; GRIBEL, R.; GRATTAPAGLIA, D. Análise da herança de locos microsatélites marcados com fluorescência em sumaúma (Ceiba pentandra) e estimativa de fecundação cruzada em populações naturais. In: WORKSHOP DO TALENTO ESTUDANTIL DA EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA, 4., 1999, Brasília. Anais: resumos dos trabalhos. Brasília, DF: EMBRAPA-CENARGEN, 1999. p. 67.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 18. | | NOVAES, E.; DROST, D. R.; FARMERIE, W. G.; PAPPAS JUNIOR, G. J.; GRATTAPAGLIA, D.; SEDEROFF, R. R.; KIRST, M. High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomic, v.9, p. 312, 2008.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 20. | | 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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| Registros recuperados : 48 | |
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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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