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| Registros recuperados : 6 | |
| 2. |  | SANTOS, I. G. dos; CARNEIRO, V. Q.; SANT'ANNA, I. de C.; CRUZ, C. D.; CARVALHO, C. G. P.; BORBA FILHO, A. L.; ALVES, A. D. Factor analysis and GGE biplot for environmental andgenotypic evaluation in sunflower trials. Functional Plant Breeding Journal, v. 1, n. 2, p. 29-40, 2019.| Biblioteca(s): Embrapa Soja. |
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| 3. | | 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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| 4. | | SILVA, G. N.; NASCIMENTO, M.; SANT'ANNA, I. de C.; CRUZ, C. D.; CAIXETA, E. T.; CARNEIRO, P. C. S.; ROSADO, R. D. S.; PESTANA, K. N.; ALMEIDA, D. P. de; OLIVEIRA, M. da S. Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 3, p. 186-193, mar. 2017. Título em português: Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica.| Biblioteca(s): Embrapa Café; Embrapa Unidades Centrais. |
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| 5. | | 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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| 6. | | OLIVEIRA, G. F.; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; CELERI, M. de O.; BARROSO, L. M. A.; SANT’ANNA, I. de C.; VIANA, J. M. S.; RESENDE, M. D. V. de; NASCIMENTO, M. Population size in QTL detection using quantile regression in genome‑wide association studies. Scientific Reports, v. 13, Article 9585, 2023. 10 p.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 6 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
26/01/2022 |
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Data da última atualização: |
26/01/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
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. |
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Afiliação: |
GABRIELA FRANÇA OLIVEIRA, UFV; ANA CAROLINA CAMPANA NASCIMENTO, UFV; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT'ANNA, IAC; JUAN VICENTE ROMERO, AGROSAVIA; CAMILA FERREIRA AZEVEDO, UFV; LEONARDO LOPES BHERING, UFV; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa. |
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Título: |
Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Plos One, v. 16, n. 1, e0243666, 2021. |
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DOI: |
https://doi.org/10.1371/journal.pone.0243666 |
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Idioma: |
Inglês |
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Conteúdo: |
This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios. |
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Thesagro: |
Regressão Linear; Seleção Genótipa. |
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Thesaurus NAL: |
Genomics; Plant breeding; Plant selection guides. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230507/1/Quantile-regression-in-genomic.pdf
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Marc: |
LEADER 02224naa a2200277 a 4500
001 2139325
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008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1371/journal.pone.0243666$2DOI
100 1 $aOLIVEIRA, G. F.
245 $aQuantile regression in genomic selection for oligogenic traits in autogamous plants$ba simulation study.$h[electronic resource]
260 $c2021
520 $aThis study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
650 $aGenomics
650 $aPlant breeding
650 $aPlant selection guides
650 $aRegressão Linear
650 $aSeleção Genótipa
700 1 $aNASCIMENTO, A. C. C.
700 1 $aNASCIMENTO, M.
700 1 $aSANT'ANNA, I. de C.
700 1 $aROMERO, J. V.
700 1 $aAZEVEDO, C. F.
700 1 $aBHERING, L. L.
700 1 $aCAIXETA, E. T.
773 $tPlos One$gv. 16, n. 1, e0243666, 2021.
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Embrapa Café (CNPCa) |
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