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| Registros recuperados : 48 | |
| 41. |  | 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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| 42. | | LOPES, M. T. G.; AGUIAR, A. V. de; GAIOTTO, F. A.; FAHRENKROG, A.; BITTENCOURT, F.; DERVINIS, C.; MÜLLER, B. S. F.; SANTOS, R. F. dos; QUISEN, R. C.; KIRST, M. Next generation transcriptome assembly for Euterpe oleracea. In: GLOBAL CONFERENCE ON PLANT SCIENCE AND MOLECULAR BIOLOGY, 2., 2018, Rome. Accentuate innovations and emerging novel research in plant sciences: book of abstracts. Rome: 2018. p. 94.| Biblioteca(s): Embrapa Florestas. |
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| 43. | | 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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| 44. | | 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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| 45. | | MÜLLER, B. S. F.; NEVES, L. G.; ALMEIDA FILHO, J. E. de; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. R.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; KIRST, M.; GRATTAPAGLIA, D. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics, v. 18, article 524, 2017. 17 p.| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| 46. | | 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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| 47. | | MUNOZ, P. R.; RESENDE JUNIOR, M. F. R.; HUBER, D. A.; QUESADA, T.; RESENDE, M. D. V. de; NEALE, D. B.; WEGRZYN, J. L.; KIRST, M.; PETER, G. F. Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Science, v. 54, p. 115-1123, May/June 2014.| Biblioteca(s): Embrapa Florestas. |
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| 48. | | MARON, L. G.; GUIMARAES, C. T.; KIRST, M.; ALBERT, P. S.; BIRCHLER, J. A.; BRADBURY, P. J.; BUCKLER, E. S.; COLUCCIO, A. E.; DANILOVA, T. V.; KUDMA, D.; MAGALHAES, J. V.; PIÑEROS, M. A.; SCHATZ, M. C.; WING, R. A.; KOCHIAN, L. V. Aluminum tolerance in maize is associated with higher MATE 1 gene copy number. Proceedings of the National Academy of Sciences of the United States of America, Washington,v. 110, n. 13, p. 5241-5246, Mar. 2013.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 48 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
19/12/2014 |
|
Data da última atualização: |
24/08/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
|
Autoria: |
MUNOZ, P. R.; RESENDE JUNIOR, M. F. R.; HUBER, D. A.; QUESADA, T.; RESENDE, M. D. V. de; NEALE, D. B.; WEGRZYN, J. L.; KIRST, M.; PETER, G. F. |
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Afiliação: |
Patricio R. Munoz, University of Florida; Marcio F. R. Resende Junior, University of Florida; Dudley A. Huber, University of Florida; Tania Quesada, University of Florida; MARCOS DEON VILELA DE RESENDE, CNPF; David B. Neale, University of California; Jill L. Wegrzyn, University of California; Matias Kirst, University of Florida; Gary F. Peter, University of Florida. |
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Título: |
Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. |
|
Ano de publicação: |
2014 |
|
Fonte/Imprenta: |
Crop Science, v. 54, p. 115-1123, May/June 2014. |
|
Idioma: |
Inglês |
|
Conteúdo: |
Quantitative genetic analyses aim to estimate genetic parameters and breeding values to select superior parents, families, and individuals. For these estimates a relationship matrix derived from the pedigree typically is used in a mixed model framework. However, breeding is a complex, multistep process and errors in the pedigree are common. Because errors reduce the accuracy of genetic parameter estimates and affect genetic gain, it is important to correct these errors. Here we show that a realized relationship matrix (RRM) derived from single nucleotide polymorphism markers based on the normality of the relationship coefficients can be used to correct pedigree errors. For a loblolly pine (Pinus taeda L.) breeding population, errors in the pedigree were detected and corrected with the RRM. With the corrected pedigree, best linear unbiased predictor (BLUP) models fit the data significantly better for 14 out of 15 traits evaluated, and the predictive ability of the genomic selection models using ridge regression BLUP increased for 13 traits. The corrected pedigree based on the normality of the relationship coefficients improves accuracy of traditional estimations of heritability and breeding values as well as genomic selection predictions. As more breeding programs begin to use genomic selection, we recommend first using the dense panel of markers to correct pedigree errors and then using the improved information to develop genomic selection prediction models. |
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Palavras-Chave: |
Genética quantitativa; Melhoramento genético. |
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Thesagro: |
Parâmetro Genético. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/114177/1/2014-API-Deon-GenomicRelationship.pdf
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|
Marc: |
LEADER 02285naa a2200253 a 4500
001 2003359
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100 1 $aMUNOZ, P. R.
245 $aGenomic relationship matrix for correcting pedigree errors in breeding populations$bimpact on genetic parameters and genomic selection accuracy.$h[electronic resource]
260 $c2014
520 $aQuantitative genetic analyses aim to estimate genetic parameters and breeding values to select superior parents, families, and individuals. For these estimates a relationship matrix derived from the pedigree typically is used in a mixed model framework. However, breeding is a complex, multistep process and errors in the pedigree are common. Because errors reduce the accuracy of genetic parameter estimates and affect genetic gain, it is important to correct these errors. Here we show that a realized relationship matrix (RRM) derived from single nucleotide polymorphism markers based on the normality of the relationship coefficients can be used to correct pedigree errors. For a loblolly pine (Pinus taeda L.) breeding population, errors in the pedigree were detected and corrected with the RRM. With the corrected pedigree, best linear unbiased predictor (BLUP) models fit the data significantly better for 14 out of 15 traits evaluated, and the predictive ability of the genomic selection models using ridge regression BLUP increased for 13 traits. The corrected pedigree based on the normality of the relationship coefficients improves accuracy of traditional estimations of heritability and breeding values as well as genomic selection predictions. As more breeding programs begin to use genomic selection, we recommend first using the dense panel of markers to correct pedigree errors and then using the improved information to develop genomic selection prediction models.
650 $aParâmetro Genético
653 $aGenética quantitativa
653 $aMelhoramento genético
700 1 $aRESENDE JUNIOR, M. F. R.
700 1 $aHUBER, D. A.
700 1 $aQUESADA, T.
700 1 $aRESENDE, M. D. V. de
700 1 $aNEALE, D. B.
700 1 $aWEGRZYN, J. L.
700 1 $aKIRST, M.
700 1 $aPETER, G. F.
773 $tCrop Science$gv. 54, p. 115-1123, May/June 2014.
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