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| Registros recuperados : 92 | |
| 61. |  | 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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| 62. | | LIMA L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; SUELA, M. M.; NASCIMENTO, M.; VIANA, J. M. S. New insights into genomic selection through population-based non-parametric prediction methods. Scientia Agricicola, v. 76, n. 4, p. 290-298, July/Aug. 2019.| Biblioteca(s): Embrapa Florestas. |
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| 63. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F.; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; OLIVEIRA, E. J. de. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program. Genetics and Molecular Research, v. 15, n. 4, gmr.15048838, Oct. 2016.| Biblioteca(s): Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
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| 64. | | AZEVEDO, C. F.; ABAD, J. I. M.; MISSIAGGIA, A. A.; AGUIAR, A. M.; RESENDE, M. D. V. de; SILVA, F. F. e.; PEREIRA, C. S. Parametrizações em marcadores dominantes DarTs para características de crescimento de eucalipto. Revista Matemática e Estatística em foco, v. 1, n. 2, 2013. Edição especial dos anais do Encontro Mineiro de Estatística, 12., 2013, Uberlândia. Resumo.| Biblioteca(s): Embrapa Florestas. |
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| 65. | | AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; NASCIMENTO, M.; VIANA, J. M. S.; VALENTE, M. S. F. Population structure correction for genomic selection through eigenvector covariates. Crop Breeding and Applied Biotechnology, Viçosa, v. 17, n. 4, p.350-358, Oct./Dec. 2017.| Biblioteca(s): Embrapa Florestas. |
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| 66. | | AZEVEDO, C. F.; SILVA, F. F. e; RESENDE, M. D. V. de; PETERNELLI, L. A.; GUIMARÃES, S. E. F.; LOPES, P. S. Quadrados mínimos parciais uni e multivariado aplicados na seleção genômica para características de carcaça em suínos. Ciência Rural, Santa Maria, RS, v. 43, n. 9, p. 1642-1649, set. 2013.| Biblioteca(s): Embrapa Florestas. |
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| 67. | | OLIVEIRA, G. F.; NASCIMENTO, A. C. C.; NASCIMENTO, M.; SANT'ANNA, I. de C.; ROMERO, J. V.; AZEVEDO, C. F.; BHERING, L. L.; CAIXETA, E. T. Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. Plos One, v. 16, n. 1, e0243666, 2021.| Biblioteca(s): Embrapa Café. |
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| 68. | | RESENDE, M. D. V. de; OLIVEIRA, A. C. B. de; CAIXETA, E. T.; ALKIMIM, E. R.; SOUSA, T. V.; PEREIRA, A. A.; ALVES, R. S.; AZEVEDO, C. F. Tamanho amostral e detecção de genes via GWAS em características quantitativas do cafeeiro. Brasília, DF: Embrapa Café, 2022. 23 p. (Embrapa Café. Circular técnica, 7).| Biblioteca(s): Embrapa Café. |
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| 69. | | SILVA, F. F.; JEREZ, E. A. Z.; RESENDE, M. D. V. de; VIANA, J. M. S.; AZEVEDO, C. F.; LOPES, P. S.; NASCIMENTO, M.; LIMA, R. O. de; GUIMARÃES, S. E. F. Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding. Journal of Applied Animal Research, v. 46, n. 1, p. 873-878, 2018.| Biblioteca(s): Embrapa Florestas. |
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| 70. | | TORRES, L. G.; OLIVEIRA, E. J. de; OGBONNA, A. C.; BAUCHET, G. J.; MUELLER, L. A.; AZEVEDO, C. F.; SILVA, F. F.; SIMIQUELI, G. F.; RESENDE, M. D. V. de. Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. Frontiers in Plant Science, v. 12, 742638, 2021.| Biblioteca(s): Embrapa Café; Embrapa Mandioca e Fruticultura. |
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| 71. | | OLIVEIRA, M. I. P. de; ROCHA, M. do S.; LUCENA, A. M. A. de; AZEVEDO, C. F. de; ARRIEL, N. H. C.; BARTOLOMEU, C. R. C.; BELTRÃO, N. E. de M. Caracterização morfo-anatômica de folhas e caule de Jatrophas curcas L. (Euphorbiacea). In: CONGRESSO BRASILEIRO DE PLANTAS OLEAGINOSAS, ÓLEOS, GORDURAS E BIODIESEL, 5.; CLÍNICA TECNOLÓGICA EM BIODIESEL, 2., 2008, Lavras. Biodiesel: tecnologia limpa. Anais...Lavras: UFLA, 2008. 9 p. Seção Trabalhos. 1 CD-ROM.| Biblioteca(s): Embrapa Algodão. |
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| 72. | | ROCHA, M. do S.; OLIVEIRA, M. I. P. de; AZEVEDO, C. F. de; LUCENA, A. M. A. de; BELTRÃO, N. E. de M.; CARVALHO, J. M. F. C.; ALMEIDA, F. de A. C.; BRUNO, R. de L. A. Caracterização morfoanatômico da cultivar BRS energia (Ricinus communis L.). In: CONGRESSO BRASILEIRO DE MAMONA, 3., 2008, Salvador. Energia e ricinoquímica: resumos. Salvador: SEAGRI: Embrapa Algodão, 2008. p. 124| Biblioteca(s): Embrapa Algodão. |
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| 73. | | ROCHA, M. do S.; OLIVEIRA, M. I. P. de; AZEVEDO, C. F. de; LUCENA, A. M. A. de; BELTRÃO, N. E. de M.; CARVALHO, J. M. F. C.; ALMEIDA, F. de A. C.; BRUNO, R. de L. A. Caracterização morfoanatômico da cultivar BRS energia (Ricinus communis L.). In: CONGRESSO BRASILEIRO DE MAMONA, 3., 2008, Salvador. Energia e ricinoquímica: anais. Salvador: SEAGRI: Embrapa Algodão, 2008. 1 CD-ROM| Biblioteca(s): Embrapa Algodão. |
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| 74. | | NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. Regularized quantile regression applied to genome-enabled prediction of quantitative traits. Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. 12 p.| Biblioteca(s): Embrapa Florestas. |
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| 75. | | BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F.; SERÃO, N. V. L.; CRUZ, C. D.; RESENDE, M. D. V. de; SILVA, F. L.; AZEVEDO, C. F.; LOPES, P. S.; GUIMARÃES, S. E. F. Regularized quantile regression for SNP marker estimation of pig growth curves. Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017. 9 p.| Biblioteca(s): Embrapa Florestas. |
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| 76. | | SOUSA, I. C. de; NASCIMENTO, M.; SANT’ANNA, I. de C.; CAIXETA, E. T.; AZEVEDO, C. F.; CRUZ, C. D.; SILVA, F. L. da; ALKIMIM, E. R.; NASCIMENTO, A. C. C.; SERÃO, N. V. L. Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. Plos One, v. 17, n.1, e0262055, 2022.| Biblioteca(s): Embrapa Café. |
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| 77. | | MIRANDA, T. L. R.; RESENDE, M. D. V. de; AZEVEDO, C. F.; NUNES, A. C. P.; TAKAHASHI, E. K.; SIMIQUELI, G. F.; SILVA, F. F. e; ALVES, R. S. Evaluation of a new additive-dominance genomic model and implications for quantitative genetics and genomic selection. Scientia Agricola, v. 79, n. 6, p. 1-7, 2022.| Biblioteca(s): Embrapa Café. |
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| 78. | | ROCHA, M. do S.; OLIVEIRA, M. I. P. de; MEDEIROS, C.; AZEVEDO, C. F. de; BELTRÃO, N. E. de M.; CARVALHO, J. M. F. C.; ALMEIDA, F. de A. C.; NASCIMENTO, L. C.; BRUNO, R. de L. A. Fungos associados a sementes de mamoneira cultivadas na região de Barbalha, CE, safra 2007. In: CONGRESSO BRASILEIRO DE MAMONA, 3., 2008, Salvador. Energia e ricinoquímica: resumos. Salvador: SEAGRI: Embrapa Algodão, 2008. p. 50| Biblioteca(s): Embrapa Algodão. |
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| 79. | | ROCHA, M. do S.; OLIVEIRA, M. I. P. de; MEDEIROS, C.; AZEVEDO, C. F. de; BELTRÃO, N. E. de M.; CARVALHO, J. M. F. C.; ALMEIDA, F. de A. C.; NASCIMENTO, L. C.; BRUNO, R. de L. A. Fungos associados a sementes de mamoneira cultivadas na região de Barbalha, CE, safra 2007. In: CONGRESSO BRASILEIRO DE MAMONA, 3., 2008, Salvador. Energia e ricinoquímica: anais. Salvador: SEAGRI: Embrapa Algodão, 2008. 1 CD-ROM| Biblioteca(s): Embrapa Algodão. |
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| 80. | | RESENDE, R. T.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; MELO, L. C.; PEREIRA, H. S.; SOUZA, T. L. P. O. de; VALDISSER, P. A. M. R.; BRONDANI, C.; VIANELLO, R. P. Genome-wide association and regional heritability mapping of plant architecture, lodging and productivity in Phaseolus vulgaris. G3: Genes, Genomes, Genetics, v. 8, p. 2841-2854, Aug. 2018.| Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Florestas. |
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| Registros recuperados : 92 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
03/01/2018 |
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Data da última atualização: |
11/01/2018 |
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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: |
NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. |
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Afiliação: |
M. Nascimento, UFV; F. F. e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; C. D. Cruz, UFV; A. C. C. Nascimento, UFV; J. M. S. Viana, UFV; C. F. Azevedo, UFV; L. M. A. Barroso, UFV. |
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Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
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Páginas: |
12 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. MenosGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved be... Mostrar Tudo |
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Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
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Thesagro: |
Estatística. |
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Thesaurus NAL: |
Marker-assisted selection; Simulation models; Statistics. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
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Marc: |
LEADER 02634naa a2200313 a 4500
001 2084109
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008 2017 bl uuuu u00u1 u #d
100 1 $aNASCIMENTO, M.
245 $aRegularized quantile regression applied to genome-enabled prediction of quantitative traits.$h[electronic resource]
260 $c2017
300 $a12 p.
520 $aGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.
650 $aMarker-assisted selection
650 $aSimulation models
650 $aStatistics
650 $aEstatística
653 $aGenomic selection
653 $aRegularized regression
653 $aSeleção genômica
653 $aSNP effects
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aVIANA, J. M. S.
700 1 $aAZEVEDO, C. F.
700 1 $aBARROSO, L. M. A.
773 $tGenetics and Molecular Research$gv. 16, n. 1, gmr16019538, 2017.
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Embrapa Florestas (CNPF) |
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