| |
|
|
| Registros recuperados : 6 | |
| 1. |  | SILVA, P. V.; FONSECA, I.; GUIMARÃES, S. E. F.; GUIMARÃES, M. F. M.; SERÃO, N. V. L.; LOPES, M. S. Expressão relativa da IL - 10 em células do leite de vacas holandesas saudáveis e com mastite clínica. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA. 45., 2008, Lavras, MG. Anais... Viçosa, MG: Sociedade Brasileira de Zootecnia, 2008.| Biblioteca(s): Embrapa Gado de Leite. |
|    |
| 3. | | FURTADO, C. L. M.; SILVA, P. V.; GUIMARAES, M. F. M.; SERÃO, N. V. L.; GUIMARÃES, J. D.; GUIMARÃES, S. E. F. Differential expression of genes in follicular cells of swines. Revista Brasileira de Zootecnia, v. 39,n. 5, p. p.1023-1028, 2010.| Biblioteca(s): Embrapa Gado de Leite. |
| |
| 4. | | 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. |
| |
| 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. |
| |
| 6. | | ALMEIDA, V. V.; SILVA, J. P. M.; SCHINCKEL, A. P.; MEIRA, A. N.; MOREIRA, G. C. M.; GOMES, J. D.; POLETI, M. D.; DARGELIO, M. D. B.; PATINHO, I.; CONTRERAS-CASTILLO, C. J.; COUTINHO, L. L.; MOURÃO, G. B.; REECY, J. M.; KOLTES, D.; SERÃO, N. V. L.; REGITANO, L. C. de A.; FUKUMASU, H.; BRUSTOLINI, A. P. L.; ALENCAR, S. M.; LUCHIARI FILHO, A.; CESAR, A. S. M. Effects of increasing dietary oil inclusion from different sources on growth performance, carcass and meat quality traits, and fatty acid profile in genetically lean immunocastrated male pigs. Livestock Science, v.248, jun. 2021, 104515. 10 p.| Biblioteca(s): Embrapa Pecuária Sudeste. |
|  |
| Registros recuperados : 6 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
03/01/2018 |
|
Data da última atualização: |
03/01/2018 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
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. |
|
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. |
|
Título: |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017. |
|
Páginas: |
9 p. |
|
DOI: |
10.1186/s40104-017-0187-z |
|
Idioma: |
Inglês |
|
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 |
|
Palavras-Chave: |
Genome association; Growth curves; Pig; QTL; Regularized quantile regression. |
|
Thesagro: |
Melhoramento genético animal; Porco; Suíno. |
|
Thesaurus NAL: |
Swine. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170172/1/2017-M.Deon-JASB-Regularized.pdf
|
|
Marc: |
LEADER 02860naa a2200373 a 4500
001 2084057
005 2018-01-03
008 2017 bl uuuu u00u1 u #d
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.
700 1 $aAZEVEDO, C. F.
700 1 $aLOPES, P. S.
700 1 $aGUIMARÃES, S. E. F.
773 $tJournal of Animal Science and Biotechnology$gv. 8, n. 59, 2017.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
