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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
20/08/2020 |
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Data da última atualização: |
20/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KRAUSE, M. D.; DIAS, K. O. das G.; SANTOS, J. P. R. dos; OLIVEIRA, A. A. de; GUIMARAES, L. J. M.; PASTINA, M. M.; MARGARIDO, G. R. A.; GARCIA, A. A. F. |
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Afiliação: |
Matheus Dalsente Krause, Iowa State University; Kaio Olímpio das Graças Dias, Escola Superior de Agricultura "Luiz de Queiroz"; Jhonathan Pedroso Rigal dos Santos, Escola Superior de Agricultura "Luiz de Queiroz"; Amanda Avelar de Oliveira, Escola Superior de Agricultura "Luiz de Queiroz"; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; Gabriel Rodrigues Alves Margarido, Escola Superior de Agricultura "Luiz de Queiroz"; Antonio Augusto Franco Garcia, Escola Superior de Agricultura "Luiz de Queiroz". |
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Título: |
Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Crop Science, v. 60, n. 6, p. 3049-3065, 2020. |
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DOI: |
10.1002/csc2.20253 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield. |
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Thesagro: |
Genótipo; Melhoramento Genético Vegetal; Milho. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219490/1/Boosting-predictive.pdf
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Marc: |
LEADER 02201naa a2200253 a 4500
001 2124456
005 2020-12-20
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1002/csc2.20253$2DOI
100 1 $aKRAUSE, M. D.
245 $aBoosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models.$h[electronic resource]
260 $c2020
520 $aGenomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield.
650 $aGenótipo
650 $aMelhoramento Genético Vegetal
650 $aMilho
700 1 $aDIAS, K. O. das G.
700 1 $aSANTOS, J. P. R. dos
700 1 $aOLIVEIRA, A. A. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
700 1 $aMARGARIDO, G. R. A.
700 1 $aGARCIA, A. A. F.
773 $tCrop Science$gv. 60, n. 6, p. 3049-3065, 2020.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
26/04/2010 |
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Data da última atualização: |
16/02/2024 |
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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: |
GIBBS, R. A.; TAYLOR, J. F.; VAN TASSELL, C. P.; BARENDSE, W.; EVERSOLE, K. A.; GILL, C. A.; GREEN, R. D.; HAMERNIK, D. L.; KAPPES, S. M.; LIEN, S.; MATUKUMALLI, L. K.; MCEWAN, J. C.; NAZARETH, L. V.; SCHNABEL, R. D.; WEINSTOCK, G. M.; WHEELER, D. A.; AJMONE-MARSAN, P.; BOETTCHER, P. J.; CAETANO, A. R.; GARCIA, J. F.; HANOTTE, O.; MARIANI, P.; SKOW, L. C.; SONSTEGARD, T. S.; WILLIAMS, J. L.; DIALLO, B.; HAILEMARIAM, L.; MARTINEZ, M. L.; MORRIS, C. A.; SILVA, L. O. C. da; SPELMAN, R. J.; MULATU, W.; ZHAO, K.; ABBEY, C. A.; AGABA, M.; ARAUJO, F. R.; BUNCH, R. J.; BURTON, J.; GORNI, C.; OLIVIER, H.; HARRISON, B. E.; LUFF, B.; MACHADO, M. A.; MWAKAYA, J.; PLASTOW, G.; SIM, W.; SMITH, T.; THOMAZ, M. B.; VALENTINI, A.; WILLIAMS, P.; WOMACK, J.; WOOLLIAMS, J. A.; LIU, Y.; QIN, X.; WORLEY, K. C.; GAO, C.; JIANG, H.; MOORE, S. S.; REN, Y.; SONG, X.-Z.; BUSTAMANTE, C. D.; HERNANDEZ, R. D.; MUZNY, D. M.; PATIL, S.; SAN LUCAS, A.; FU, Q.; KENT, M. P.; VEGA, R.; MATUKUMALLI, A.; MCWILLIAM, S.; SCLEP, G.; BRYC, K.; CHOI, J.; GAO, H.; GREFENSTETTE, J. J.; MURDOCH, B.; STELLA, A.; VILLA-ANGULO, R.; WRIGHT, M.; AERTS, J.; JANN, O.; NEGRINI, R.; GODDARD, M. E.; HAYES, B. J.; BRADLEY, D. G.; SILVA, M. V. G. B.; LAU, L. P. L.; LIU, G. E.; LYNN, D. J.; PANZITTA, F.; DODDS, K. G. |
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Afiliação: |
RICHARD A. GIBBS; JEREMY F. TAYLOR; CURTIS P. VAN TASSELL; WILLIAM BARENDSE; KELLYE A. EVERSOLE; CLARE A. GILL; RONNIE D. GREEN; DEBORA L. HAMERNIK; STEVEN M. KAPPES; SIGBJØRN LIEN; LAKSHMI K. MATUKUMALLI; JOHN C. MCEWAN; LYNNE V. NAZARETH; ROBERT D. SCHNABEL; GEORGE M. WEINSTOCK; DAVID A. WHEELER; PAOLO AJMONE-MARSAN; PAUL J. BOETTCHER; ALEXANDRE R. CAETANO; JOSE FERNANDO GARCIA; OLIVIER HANOTTE; PAOLA MARIANI; LOREN C. SKOW; TAD S. SONSTEGARD; JOHN L. WILLIAMS; BOUBACAR DIALLO; LEMECHA HAILEMARIAM; MARIO L MARTINEZ; CHRIS A MORRIS; LUIZ OTAVIO CAMPOS DA SILVA, CNPGC; RICHARD J SPELMAN; WOUDYALEW MULATU; KEYAN ZHAO; COLETTE A ABBEY; MORRIS AGABA; FLABIO RIBEIRO DE ARAUJO, CNPGC; ROWAN J BUNCH; JAMES BURTON; CHIARA GORNI; HANOTTE OLIVIER; BLAIR E HARRISON; BILL LUFF; MARCO ANTONIO MACHADO, CNPGL; JOEL MWAKAYA; GRAHAM PLASTOW; WARREN SIM; TIMOTHY SMITH; MERLE B THOMAS; ALESSIO VALENTINI; PAUL WILLIAMS; JAMES WOMACK; JOHN A WOOLLIAMS; YUE LIU; XIANG QIN; KIM C WORLEY; CHUAN GAO; HUAIYANG JIANG; STEPHEN S. MOORE; YANRU REN; XING-ZHI SONG; CARLOS D. BUSTAMANTE; RYAN D. HERNANDEZ; DONNA M. MUZNY; SHOBHA PATIL; ANTHONY SAN LUCAS; QING FU; MATTHEW P. KENT; RICHARD VEGA; ARUNA MATUKUMALLI; SEAN MCWILLIAM; GERT SCLEP; KATARZYNA BRYC; JUNGWOO CHOI; HONG GAO; JOHN J. GREFENSTETTE; BRENDA MURDOCH; ALESSANDRA STELLA; RAFAEL VILLA-ANGULO; MARK WRIGHT; JAN AERTS; OLIVER JANN; RICCARDO NEGRINI; MIKE E. GODDARD; BEN J. HAYES; DANIEL G. BRADLEY; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; LILIAN P. L. LAU; GEORGE E. LIU; DAVID J. LYNN; FRANCESCA PANZITTA; KEN G. DODDS. |
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Título: |
Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
Science, v. 324, n. 5926, p. 528-532, 2009. |
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Idioma: |
Inglês |
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Conteúdo: |
The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans. |
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Thesagro: |
Genoma; Melhoramento Genético Animal. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/711673/1/Genome-Wide-survey-of-SNP-variation-uncovers-the-genetic-structure-of-cattle-breeds.pdf
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Marc: |
LEADER 03926naa a2201225 a 4500
001 1711673
005 2024-02-16
008 2009 bl uuuu u00u1 u #d
100 1 $aGIBBS, R. A.
245 $aGenome-wide survey of SNP variation uncovers the genetic structure of cattle breeds.$h[electronic resource]
260 $c2009
520 $aThe imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.
650 $aGenoma
650 $aMelhoramento Genético Animal
700 1 $aTAYLOR, J. F.
700 1 $aVAN TASSELL, C. P.
700 1 $aBARENDSE, W.
700 1 $aEVERSOLE, K. A.
700 1 $aGILL, C. A.
700 1 $aGREEN, R. D.
700 1 $aHAMERNIK, D. L.
700 1 $aKAPPES, S. M.
700 1 $aLIEN, S.
700 1 $aMATUKUMALLI, L. K.
700 1 $aMCEWAN, J. C.
700 1 $aNAZARETH, L. V.
700 1 $aSCHNABEL, R. D.
700 1 $aWEINSTOCK, G. M.
700 1 $aWHEELER, D. A.
700 1 $aAJMONE-MARSAN, P.
700 1 $aBOETTCHER, P. J.
700 1 $aCAETANO, A. R.
700 1 $aGARCIA, J. F.
700 1 $aHANOTTE, O.
700 1 $aMARIANI, P.
700 1 $aSKOW, L. C.
700 1 $aSONSTEGARD, T. S.
700 1 $aWILLIAMS, J. L.
700 1 $aDIALLO, B.
700 1 $aHAILEMARIAM, L.
700 1 $aMARTINEZ, M. L.
700 1 $aMORRIS, C. A.
700 1 $aSILVA, L. O. C. da
700 1 $aSPELMAN, R. J.
700 1 $aMULATU, W.
700 1 $aZHAO, K.
700 1 $aABBEY, C. A.
700 1 $aAGABA, M.
700 1 $aARAUJO, F. R.
700 1 $aBUNCH, R. J.
700 1 $aBURTON, J.
700 1 $aGORNI, C.
700 1 $aOLIVIER, H.
700 1 $aHARRISON, B. E.
700 1 $aLUFF, B.
700 1 $aMACHADO, M. A.
700 1 $aMWAKAYA, J.
700 1 $aPLASTOW, G.
700 1 $aSIM, W.
700 1 $aSMITH, T.
700 1 $aTHOMAZ, M. B.
700 1 $aVALENTINI, A.
700 1 $aWILLIAMS, P.
700 1 $aWOMACK, J.
700 1 $aWOOLLIAMS, J. A.
700 1 $aLIU, Y.
700 1 $aQIN, X.
700 1 $aWORLEY, K. C.
700 1 $aGAO, C.
700 1 $aJIANG, H.
700 1 $aMOORE, S. S.
700 1 $aREN, Y.
700 1 $aSONG, X.-Z.
700 1 $aBUSTAMANTE, C. D.
700 1 $aHERNANDEZ, R. D.
700 1 $aMUZNY, D. M.
700 1 $aPATIL, S.
700 1 $aSAN LUCAS, A.
700 1 $aFU, Q.
700 1 $aKENT, M. P.
700 1 $aVEGA, R.
700 1 $aMATUKUMALLI, A.
700 1 $aMCWILLIAM, S.
700 1 $aSCLEP, G.
700 1 $aBRYC, K.
700 1 $aCHOI, J.
700 1 $aGAO, H.
700 1 $aGREFENSTETTE, J. J.
700 1 $aMURDOCH, B.
700 1 $aSTELLA, A.
700 1 $aVILLA-ANGULO, R.
700 1 $aWRIGHT, M.
700 1 $aAERTS, J.
700 1 $aJANN, O.
700 1 $aNEGRINI, R.
700 1 $aGODDARD, M. E.
700 1 $aHAYES, B. J.
700 1 $aBRADLEY, D. G.
700 1 $aSILVA, M. V. G. B.
700 1 $aLAU, L. P. L.
700 1 $aLIU, G. E.
700 1 $aLYNN, D. J.
700 1 $aPANZITTA, F.
700 1 $aDODDS, K. G.
773 $tScience$gv. 324, n. 5926, p. 528-532, 2009.
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Embrapa Gado de Leite (CNPGL) |
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