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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
18/03/2016 |
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Data da última atualização: |
18/04/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
TIAN, J.; DIAO, H.; LIANG, L.; ARTHURS, S.; HAO, C.; MASCARIN, G. M.; MA, R. |
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Afiliação: |
JING TIAN, SHANXI AGRICULTURAL UNIVERSITY, China; HONGLIANG DIAO, SHANXI AGRICULTURAL UNIVERSITY, China; LI LIANG, SHANXI AGRICULTURAL UNIVERSITY, China; STEVEN ARTHURS, UNIVERSITY OF FLORIDA, USA; CHI HAO, SHANXI AGRICULTURAL UNIVERSITY, China; GABRIEL MOURA MASCARIN, CNPAF; RUIYAN MA, SHANXI AGRICULTURAL UNIVERSITY, China. |
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Título: |
Host plants influence susceptibility of whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) to the entomopathogenic fungus Isaria fumosorosea (Hypocreales: Cordycipitaceae). |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Biocontrol Science and Technology, v. 26, n. 4, p. 528-538, 2016. |
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DOI: |
10.1080/09583157.2015.1129393 |
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Idioma: |
Inglês |
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Conteúdo: |
We quantified the tritrophic effect of host plant on the susceptibility of the sweetpotato whitefly Bemisia tabaci (Genn.) to a fungal pathogen in the laboratory. Second-instar whiteflies reared on cucumber, eggplant, tomato and bean plants for six generations were exposed to conidial suspensions of Isaria fumosorosea isolate IF-1106. Our results did not detect differences in response (proportional survival or median lethal time, LT50 days) among insect populations derived from different plants that were treated with 107 conidia/ml. However, at concentrations <- 5×106 conidia/ml, whiteflies reared on bean and tomato died significantly more quickly (i.e. LT50 of 4?5 days) compared with cucumber and eggplant reared populations (5?7 days). Bean and tomato-reared populations were also more susceptible to mycosis (LC50 = 6 × 105 conidia/ml) compared with those reared on cucumber (1.9 × 106 conidia/ml) and eggplant (1.5 × 106 conidia/ml). A separate study confirmed that this differential response of whitefly populations to I. fumosorosea was not explained by differences in deposition rate of conidia on leaf surfaces (i.e. a dosage effect). Our findings show that host plants affect the pathogenicity and virulence of a herbivore pathogen, but depend on the rate of exposure (inoculum) applied. |
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Thesagro: |
Bemisia tabaci; Fungo entomogeno; Mosca branca. |
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Thesaurus Nal: |
Isaria fumosorosea. |
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Categoria do assunto: |
O Insetos e Entomologia |
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Marc: |
LEADER 02125naa a2200253 a 4500
001 2041326
005 2016-04-18
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1080/09583157.2015.1129393$2DOI
100 1 $aTIAN, J.
245 $aHost plants influence susceptibility of whitefly Bemisia tabaci (Hemiptera$bAleyrodidae) to the entomopathogenic fungus Isaria fumosorosea (Hypocreales: Cordycipitaceae).$h[electronic resource]
260 $c2016
520 $aWe quantified the tritrophic effect of host plant on the susceptibility of the sweetpotato whitefly Bemisia tabaci (Genn.) to a fungal pathogen in the laboratory. Second-instar whiteflies reared on cucumber, eggplant, tomato and bean plants for six generations were exposed to conidial suspensions of Isaria fumosorosea isolate IF-1106. Our results did not detect differences in response (proportional survival or median lethal time, LT50 days) among insect populations derived from different plants that were treated with 107 conidia/ml. However, at concentrations <- 5×106 conidia/ml, whiteflies reared on bean and tomato died significantly more quickly (i.e. LT50 of 4?5 days) compared with cucumber and eggplant reared populations (5?7 days). Bean and tomato-reared populations were also more susceptible to mycosis (LC50 = 6 × 105 conidia/ml) compared with those reared on cucumber (1.9 × 106 conidia/ml) and eggplant (1.5 × 106 conidia/ml). A separate study confirmed that this differential response of whitefly populations to I. fumosorosea was not explained by differences in deposition rate of conidia on leaf surfaces (i.e. a dosage effect). Our findings show that host plants affect the pathogenicity and virulence of a herbivore pathogen, but depend on the rate of exposure (inoculum) applied.
650 $aIsaria fumosorosea
650 $aBemisia tabaci
650 $aFungo entomogeno
650 $aMosca branca
700 1 $aDIAO, H.
700 1 $aLIANG, L.
700 1 $aARTHURS, S.
700 1 $aHAO, C.
700 1 $aMASCARIN, G. M.
700 1 $aMA, R.
773 $tBiocontrol Science and Technology$gv. 26, n. 4, p. 528-538, 2016.
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Embrapa Arroz e Feijão (CNPAF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
08/11/2019 |
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Data da última atualização: |
08/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
ALMEIDA FILHO, J. E. de A.; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JÚNIOR, M. F. R. de. |
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Afiliação: |
Janeo Eustáquio de Almeida Filho, Universidade Esatdual do Norte Fluminense e "Darcy Ribeiro"; João Filipi Rodrigues Guimarães, Futuragene Ltda; Fabyano Fonsceca e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Patricio Muñoz, University of Florida; Matias Kirst, University of Florida; Marcio Fernando Ribeiro de Resende Júnior, University of Florida. |
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Título: |
Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019. |
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Idioma: |
Inglês |
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Conteúdo: |
The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits MenosThe genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and ... Mostrar Tudo |
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Palavras-Chave: |
BayesA; Genomic Prediction; Genotypic Value; GenPred; Oligogenic; Polygenic; Predição genòmica; RKHS; Shared Data Resources. |
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Thesagro: |
Genótipo. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02704naa a2200313 a 4500
001 2114084
005 2019-11-08
008 2019 bl uuuu u00u1 u #d
100 1 $aALMEIDA FILHO, J. E. de A.
245 $aGenomic prediction of additive and non-additive effects using genetic markers and pedigrees.$h[electronic resource]
260 $c2019
520 $aThe genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits
650 $aGenótipo
653 $aBayesA
653 $aGenomic Prediction
653 $aGenotypic Value
653 $aGenPred
653 $aOligogenic
653 $aPolygenic
653 $aPredição genòmica
653 $aRKHS
653 $aShared Data Resources
700 1 $aGUIMARÃES, J. F. R.
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aMUÑOZ, P.
700 1 $aKIRST, M.
700 1 $aRESENDE JÚNIOR, M. F. R. de
773 $tG3: Genes, Genomes, Genetics$gv. 9, p. 2739-2748, Aug. 2019.
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