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| Registros recuperados : 13 | |
| 2. |  | TEODORO, P. E.; NASCIMENTO, M.; TORRES, F. E.; BARROSO, L. M. A.; SAGRILO, E. Perspectiva bayesiana na seleção de genótipos de feijão-caupi em ensaios de valor de cultivo e uso. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 10, p. 878-885, out. 2015.| Biblioteca(s): Embrapa Meio-Norte; Embrapa Unidades Centrais. |
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| 3. | | TEODORO, P. E.; BARROSO, L. M. A.; NASCIMENTO, M.; TORRES, F. E.; SAGRILO, E.; SANTOS, A. dos; RIBEIRO, L. P. Redes neurais artificiais para identificar genótipos de feijão-caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 11, p. 1054-1060, nov. 2015.| Biblioteca(s): Embrapa Meio-Norte; Embrapa Unidades Centrais. |
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| 5. | | NASCIMENTO, M.; ROCHA, G. S. da; PINTO, D. S.; BARROSO, L. M. A.; NASCIMENTO, A. C. C.; FERREIRA, R. de P.; SILVA, F. F. e. Correlação de Spearman aplicada ao estudo de adaptabilidade e estabilidade em genótipos de alfafa. Investigacion Agrária, v. 15, n. 2, p. 83-90, 2013.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 7. | | BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; FONSECA, F. F. e; CRUZ, C. D.; BHERING, L. L.; FERREIRA, R. de P. Metodologia para análise de adaptabilidade e estabilidade por meio de regressão quantílica. Pesquisa Agropecuária Brasileira, v. 50, n. 4, p. 290-297, abr. 2015.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 8. | | BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F. e; CRUZ, C. D.; BHERING, L. L.; FERREIRA, R. de P. Metodologia para análise de adaptabilidade e estabilidade por meio de regressão quantílica. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 4, p. 267-290-297, abr. 2015. Título em inglês: Methodology for analysis of adaptability and stability using quantile regression.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 9. | | BARROSO, L. M. A.; TEODORO, P. E.; NASCIMENTO, M.; TORRES, F. E.; SANTOS, A. dos; CORRÊA, A. M.; SAGRILO, E.; CORRÊA, C. C. G.; SILVA, F. A.; CECCON, G. Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stability. Genetics and Molecular Research, v. 15, n. 1, p. 1-11, 2016.| Biblioteca(s): Embrapa Agropecuária Oeste; Embrapa Meio-Norte. |
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| 10. | | 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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| 11. | | 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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| 12. | | 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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| 13. | | TEIXEIRA, F. R. F.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F. e; CRUZ, C. D.; AZEVEDO, C. F.; PAIXÃO, D. M.; BARROSO, L. M. A.; VERARDO, L. L.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F.; LOPES, P. S. Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. Genetics and Molecular Research, v. 15, n. 2, 2016. 10 p.| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 13 | |
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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: |
03/01/2018 |
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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: |
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. |
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Afiliação: |
L. M. A. Barroso, UFV; M. Nascimento, UFV; A. C. C. Nascimento, UFV; F. F. Silva, UFV; N. V. L. Serão, Iowa State University; C. D. Cruz, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; F. L. Silva, UFV; C. F. Azevedo, UFV; P. S. Lopes, UFV; S. E. F. Guimarães, UFV. |
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Título: |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017. |
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Páginas: |
9 p. |
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DOI: |
10.1186/s40104-017-0187-z |
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Idioma: |
Inglês |
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Conteúdo: |
Background: Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results: The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. Conclusions: RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves. MenosBackground: Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results: The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. Conclusions: RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), t... Mostrar Tudo |
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Palavras-Chave: |
Genome association; Growth curves; Pig; QTL; Regularized quantile regression. |
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Thesagro: |
Melhoramento genético animal; Porco; Suíno. |
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Thesaurus NAL: |
Swine. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170172/1/2017-M.Deon-JASB-Regularized.pdf
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Marc: |
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024 7 $a10.1186/s40104-017-0187-z$2DOI
100 1 $aBARROSO, L. M. A.
245 $aRegularized quantile regression for SNP marker estimation of pig growth curves.$h[electronic resource]
260 $c2017
300 $a9 p.
520 $aBackground: Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results: The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. Conclusions: RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.
650 $aSwine
650 $aMelhoramento genético animal
650 $aPorco
650 $aSuíno
653 $aGenome association
653 $aGrowth curves
653 $aPig
653 $aQTL
653 $aRegularized quantile regression
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aSILVA, F. F.
700 1 $aSERÃO, N. V. L.
700 1 $aCRUZ, C. D.
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. L.
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700 1 $aGUIMARÃES, S. E. F.
773 $tJournal of Animal Science and Biotechnology$gv. 8, n. 59, 2017.
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