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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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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: |
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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Registro original: |
Embrapa Florestas (CNPF) |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
20/09/2013 |
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Data da última atualização: |
27/02/2024 |
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Tipo da produção científica: |
Folder/Folheto/Cartilha |
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Autoria: |
CARNEIRO, G. E. de S.; PIPOLO, A. E.; MELO, C. L. P. de; LIMA, D. de; MIRANDA, L. C.; PETEK, M. R.; BORGES, R. de S.; GOMIDE, F. B.; DALBOSCO, M.; DENGLER, R. U. |
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Afiliação: |
GERALDO ESTEVAM DE SOUZA CARNEIRO, CNPSO; ANTONIO EDUARDO PIPOLO, CNPSO; CARLOS LASARO PEREIRA DE MELO, CPAO; DIVANIA DE LIMA, CNPSO; LUIZ CARLOS MIRANDA, SPM E Londrina; MARCOS RAFAEL PETEK, SPM E Londrina; ROGERIO DE SA BORGES, SPM E Londrina; FERNANDO BERNARDO GOMIDE, Fundação Meridional; MILTON DALBOSCO, Fundação Meridional; RALF UDO DENGLER, Fundação Meridional. |
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Título: |
Cultivares de soja: macrorregiões 1, 2, e 3 Centro-Sul do Brasil. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Londrina: Embrapa Soja, 2013. |
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Páginas: |
55 p. |
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Idioma: |
Português |
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Notas: |
Catálogo 04 publicado em setembro de 2013. |
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Conteúdo: |
Grupo de maturidade relativa. Indicação de cultivares por região edafoclimática. Época de semeadura. População de plantas e densidade de semeadura.
Peso médio de sementes. Mancha ?olho-de-rã?. Cancro da haste. Podridão radicular de fitóftora. Oídio. Nematoides de galhas. Cultivares de soja convencional. Cultivares de soja transgênica (RR). |
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Palavras-Chave: |
Cultivo. |
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Thesagro: |
Soja; Variedade. |
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Thesaurus NAL: |
Cultivars; soybeans. |
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Categoria do assunto: |
A Sistemas de Cultivo
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/89931/1/CATALOGOSoja-Centro-sul-2013.pdf
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Marc: |
LEADER 01139nam a2200301 a 4500
001 1966772
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008 2013 bl uuuu u0uu1 u #d
100 1 $aCARNEIRO, G. E. de S.
245 $aCultivares de soja$bmacrorregiões 1, 2, e 3 Centro-Sul do Brasil.
260 $aLondrina: Embrapa Soja$c2013
300 $a55 p.
500 $aCatálogo 04 publicado em setembro de 2013.
520 $aGrupo de maturidade relativa. Indicação de cultivares por região edafoclimática. Época de semeadura. População de plantas e densidade de semeadura. Peso médio de sementes. Mancha ?olho-de-rã?. Cancro da haste. Podridão radicular de fitóftora. Oídio. Nematoides de galhas. Cultivares de soja convencional. Cultivares de soja transgênica (RR).
650 $aCultivars
650 $asoybeans
650 $aSoja
650 $aVariedade
653 $aCultivo
700 1 $aPIPOLO, A. E.
700 1 $aMELO, C. L. P. de
700 1 $aLIMA, D. de
700 1 $aMIRANDA, L. C.
700 1 $aPETEK, M. R.
700 1 $aBORGES, R. de S.
700 1 $aGOMIDE, F. B.
700 1 $aDALBOSCO, M.
700 1 $aDENGLER, R. U.
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Embrapa Soja (CNPSO) |
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