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Registro Completo |
Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
15/06/1993 |
Data da última atualização: |
13/09/2004 |
Autoria: |
DEITZ, L. L.; DUYN, J. W. van; BRADLEY JUNIOR, J. R.; RABB, R. L.; BROOKS, W. M.; STINNER, R. E. |
Título: |
A guide to the identification and biology of soybean arthropods in North Carolina. |
Ano de publicação: |
1976 |
Fonte/Imprenta: |
Raleigh: North Carolina Agricultural Experiment Station, 1976. |
Páginas: |
264p. |
Série: |
(Technical Bulletin, 238). |
Idioma: |
Inglês |
Conteúdo: |
Crop characteristics; Procedures and study areas; Phytophagous insects and mites; Entomophagous arthropods and pathogens; Parasites; Insect pathogens. |
Palavras-Chave: |
Arthropod; Artropode; Biology; EUA; Guia; Guide; Identification; Insect pathogens; Insect pest; Inseto praga; Parasita; Parasite; Predator; Soybean; USA. |
Thesagro: |
Biologia; Díptera; Entomologia; Hemíptera; Identificação; Predador; Pseudoplusia Includens; Soja. |
Thesaurus Nal: |
Coleoptera; entomology; Homoptera; Hymenoptera; Lepidoptera. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01456nam a2200529 a 4500 001 1457099 005 2004-09-13 008 1976 bl uuuu de 00u1 u #d 100 1 $aDEITZ, L. L. 245 $aA guide to the identification and biology of soybean arthropods in North Carolina. 260 $aRaleigh: North Carolina Agricultural Experiment Station$c1976 300 $a264p. 490 $a(Technical Bulletin, 238). 520 $aCrop characteristics; Procedures and study areas; Phytophagous insects and mites; Entomophagous arthropods and pathogens; Parasites; Insect pathogens. 650 $aColeoptera 650 $aentomology 650 $aHomoptera 650 $aHymenoptera 650 $aLepidoptera 650 $aBiologia 650 $aDíptera 650 $aEntomologia 650 $aHemíptera 650 $aIdentificação 650 $aPredador 650 $aPseudoplusia Includens 650 $aSoja 653 $aArthropod 653 $aArtropode 653 $aBiology 653 $aEUA 653 $aGuia 653 $aGuide 653 $aIdentification 653 $aInsect pathogens 653 $aInsect pest 653 $aInseto praga 653 $aParasita 653 $aParasite 653 $aPredator 653 $aSoybean 653 $aUSA 700 1 $aDUYN, J. W. van 700 1 $aBRADLEY JUNIOR, J. R. 700 1 $aRABB, R. L. 700 1 $aBROOKS, W. M. 700 1 $aSTINNER, R. E.
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Embrapa Soja (CNPSO) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
24/10/2012 |
Data da última atualização: |
20/02/2015 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
RESENDE JUNIOR, M. F. R.; MUÑOZ, P.; RESENDE, M. D. V. de; GARRICK, D. J.; FERNANDO, R. L.; DAVIS, J. M.; JOKELA, E. J.; MARTIN, T. A.; PETER, G. F.; KIRST, M. |
Afiliação: |
M. F. R. RESENDE JUNIOR, UNIVERSITY OF FLORIDA; P. MUÑOZ, UNIVERSITY OF FLORIDA; MARCOS DEON VILELA DE RESENDE, CNPF; D. J. GARRICK, IOWA STATE UNIVERSITY; R. L. FERNANDO, IOWA STATE UNIVERSITY; J. M. DAVIS, UNIVERSITY OF FLORIDA; E. J. JOKELA, UNIVERSITY OF FLORIDA; T. A. MARTIN, UNIVERSITY OF FLORIDA; G. F. PETER, UNIVERSITY OF FLORIDA; M. KIRST, UNIVERSITY OF FLORIDA. |
Título: |
Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
Genetics, v. 190, p. 1503-1510, April 2012. |
Idioma: |
Inglês |
Conteúdo: |
Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cp, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cp, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models. MenosGenomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cp, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cp, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contrib... Mostrar Tudo |
Palavras-Chave: |
Precisão. |
Thesagro: |
Pinus Taeda; Seleção Genética. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02528naa a2200265 a 4500 001 1937733 005 2015-02-20 008 2012 bl uuuu u00u1 u #d 100 1 $aRESENDE JUNIOR, M. F. R. 245 $aAccuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.)$h[electronic resource] 260 $c2012 520 $aGenomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cp, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cp, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models. 650 $aPinus Taeda 650 $aSeleção Genética 653 $aPrecisão 700 1 $aMUÑOZ, P. 700 1 $aRESENDE, M. D. V. de 700 1 $aGARRICK, D. J. 700 1 $aFERNANDO, R. L. 700 1 $aDAVIS, J. M. 700 1 $aJOKELA, E. J. 700 1 $aMARTIN, T. A. 700 1 $aPETER, G. F. 700 1 $aKIRST, M. 773 $tGenetics$gv. 190, p. 1503-1510, April 2012.
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