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Registro Completo |
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
09/06/2010 |
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Data da última atualização: |
11/06/2010 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
PENTEADO FILHO, R. de C.; AVILA, A. F. D. |
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Afiliação: |
ROBERTO DE CAMARGO PENTEADO FILHO, SGE; ANTONIO FLAVIO DIAS AVILA, SGE. |
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Título: |
A scientometric analysis of Embrapa's Brazil web of science articles from 1977 to 2006. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON SCIENTOMETRICS AND INFORMETRICS, 12., 2009, Rio de Janeiro. Proceedings... São Paulo: BIREME/PAHO/WHO; Federal University of Rio de Janeiro, 2009. |
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Volume: |
v. 2 |
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Páginas: |
786-793 |
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Idioma: |
Português |
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Palavras-Chave: |
Bibliometria; Bibliometrical analysis; Cienciometria; Pesquisa agropecuária; Produção científica; Science in Brazil; Scientific production; Scientometry. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/18230/1/issi101PenteadoAvila.PDF
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Marc: |
LEADER 00845nam a2200229 a 4500
001 1854755
005 2010-06-11
008 2009 bl uuuu u00u1 u #d
100 1 $aPENTEADO FILHO, R. de C.
245 $aA scientometric analysis of Embrapa's Brazil web of science articles from 1977 to 2006.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON SCIENTOMETRICS AND INFORMETRICS, 12., 2009, Rio de Janeiro. Proceedings... São Paulo: BIREME/PAHO/WHO; Federal University of Rio de Janeiro$c2009
300 $a786-793 v. 2
490 $vv. 2
653 $aBibliometria
653 $aBibliometrical analysis
653 $aCienciometria
653 $aPesquisa agropecuária
653 $aProdução científica
653 $aScience in Brazil
653 $aScientific production
653 $aScientometry
700 1 $aAVILA, A. F. D.
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas; Embrapa Mandioca e Fruticultura. |
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Data corrente: |
26/07/2019 |
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Data da última atualização: |
30/10/2019 |
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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: |
LIMA, L. P.; AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; OLIVEIRA, E. J. de. |
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Afiliação: |
Leísa Pires Lima, UFV; Camila Ferreira Azevedo, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, UFC; José Marcelo Soriano Viana, UFV; EDER JORGE DE OLIVEIRA, CNPMF. |
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Título: |
Triple categorical regression for genomic selection: application to cassava breeding. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Scientia Agricola, v. 76, n. 5, p. 368-375, Sept./Oct. 2019. |
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DOI: |
10.1590/1678-992X-2017-0369 |
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Idioma: |
Inglês |
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Conteúdo: |
Genome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS. MenosGenome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The... Mostrar Tudo |
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Palavras-Chave: |
BLASSO; G-BLUP; Genética quantitativa; Genomic heritability; Genomic prediction; Herdabilidade genômica; Molecular markers; Predição genômica; Quantitative genetics theory; Ridge. |
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Thesagro: |
Marcador Molecular. |
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Thesaurus NAL: |
Prediction. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199876/1/2019-M.Deon-SA-Triple.pdf
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Marc: |
LEADER 02695naa a2200337 a 4500
001 2110879
005 2019-10-30
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1590/1678-992X-2017-0369$2DOI
100 1 $aLIMA, L. P.
245 $aTriple categorical regression for genomic selection$bapplication to cassava breeding.$h[electronic resource]
260 $c2019
520 $aGenome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.
650 $aPrediction
650 $aMarcador Molecular
653 $aBLASSO
653 $aG-BLUP
653 $aGenética quantitativa
653 $aGenomic heritability
653 $aGenomic prediction
653 $aHerdabilidade genômica
653 $aMolecular markers
653 $aPredição genômica
653 $aQuantitative genetics theory
653 $aRidge
700 1 $aAZEVEDO, C. F.
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. F. e
700 1 $aVIANA, J. M. S.
700 1 $aOLIVEIRA, E. J. de
773 $tScientia Agricola$gv. 76, n. 5, p. 368-375, Sept./Oct. 2019.
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Embrapa Florestas (CNPF) |
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