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Registro Completo |
Biblioteca(s): |
Embrapa Pecuária Sudeste; Embrapa Pecuária Sul. |
Data corrente: |
06/06/2014 |
Data da última atualização: |
16/07/2014 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
SANTANA, M. M.; SAVIAN, J. V.; BARTH NETO, A.; SILVA, B. de A.; VIEIRA, P. C.; TISCHLER, M. R.; SCHONS, R. M. T.; CEZIMBRA, I. M.; GENRO, T. C. M.; BERNDT, A.; BAYER, C.; CARVALHO, P. C. de F. |
Afiliação: |
M. Moreira Santana, UFRGS; J. Victor Savian, UFRGS; A. Barth Neto, UFRGS; B. De Araujo Silva, UFRGS; P. Cardozo Vieira, UFRGS; M. Ritzel Tischler, UFRGS; R. Marinho Três Schons, UFRGS; I. Machado Cezimbra, UFRGS; TERESA CRISTINA MORAES GENRO, CPPSUL; ALEXANDRE BERNDT, CPPSE; C. Bayer, UFRGS; P. C. De Faccio Carvalho, UFRGS. |
Título: |
Effect of herbage intake on methane emission by grazing sheep. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
In: LIVESTOCK, CLIMATE CHANGE AND FOOD SECURITY CONFERENCE, 2014, Madrid. Conference abstract book... [Paris]: INRA, 2014. |
Páginas: |
p. 75. |
Idioma: |
Inglês |
Thesagro: |
Metano; Ovino; Pastagem. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/103248/1/Santana-et-al-lccfsc75.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/115385/1/PROCI-2014.00194.pdf
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Marc: |
LEADER 00806nam a2200277 a 4500 001 1987817 005 2014-07-16 008 2014 bl uuuu u00u1 u #d 100 1 $aSANTANA, M. M. 245 $aEffect of herbage intake on methane emission by grazing sheep. 260 $aIn: LIVESTOCK, CLIMATE CHANGE AND FOOD SECURITY CONFERENCE, 2014, Madrid. Conference abstract book... [Paris]: INRA$c2014 300 $ap. 75. 650 $aMetano 650 $aOvino 650 $aPastagem 700 1 $aSAVIAN, J. V. 700 1 $aBARTH NETO, A. 700 1 $aSILVA, B. de A. 700 1 $aVIEIRA, P. C. 700 1 $aTISCHLER, M. R. 700 1 $aSCHONS, R. M. T. 700 1 $aCEZIMBRA, I. M. 700 1 $aGENRO, T. C. M. 700 1 $aBERNDT, A. 700 1 $aBAYER, C. 700 1 $aCARVALHO, P. C. de F.
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Registro original: |
Embrapa Pecuária Sul (CPPSUL) |
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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
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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.
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