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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
14/09/2020 |
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Data da última atualização: |
07/10/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KUHN, J. H.; ADKINS, S.; ALIOTO, D.; ALKHOVSKY, S. V.; AMARASINGHE, G. K.; AVSIC-ZUPANC, T.; AYLLÓN, M. A.; BAHL, J.; BALKEMA-BUSCHMANN, A.; BALLINGER, M. J.; BARTONICKA, T.; BASLER, C.; BAVARI, S.; BEER, M.; BENTE, D. A.; BERGERON, É.; BIRD, B. H.; BLAIR, C.; BLASDELL, K. R.; BRADFUTE, S. B.; BREYTA, R.; BRIESE, T.; BROWN, P. A.; BUCHHOL, U. J.; BUCHMEIER, M. J.; BUKREYEV, A.; BURT, F.; BUZKAN, N.; CALISHER, C. H.; CAO, M.; CASAS, I.; CHAMBERLAIN, O.; CHANDRAN, K.; CHARREL, R. N.; CHEN, B.; CHIUMENTI, M.; CHO, IL-R.; CLEGG, J. C. S.; CROZIER, I.; GRAÇA, J. V. da; BÓ, E. D.; DÁVILA, A. M. R.; LA TORRE, J. C. de; LAMBALLERIE, X. de; SWART, R. L. de; DI BELLO, P. L.; DI PAOLA, N.; DI SERIO, F.; DIETZGEN, R. G.; DIGIARO, M.; DOLJA, V. V.; DOLNIK, O.; DREBOT, M. A.; DREXLER, J. F.; DÜRRWALD, R.; DUFKOVA, L.; DUNDON, W. G.; DUPREX, W. P.; DYE, J. M.; EASTON, A. J.; EBIHARA, H.; ELBEAINO, T.; ERGÜNAY, K.; FERNANDES, J.; FOOK, A. R.; FORMENT, P. B. H.; FORTH, L. F.; FOUCHIER, R. A. M.; ASTUA, J. de F.; GAGO-ZACHERT, S.; GÃO, G. F.; GARCÍA, M. L.; GARCÍA-SASTRE, A.; GARRISON, A. R.; GBAKIMA, A.; GOLDSTEIN, T.; GONZALEZ, J. P. J.; GRIFFITHS, A.; GROSCHUP, M. H.; GÜNTHER, S.; GUTERRES, A.; HALL, R. A.; HAMMOND, J.; HASSAN, M.; HEPOJOKI, J.; HEPOJOKI, S.; HETZEL, U.; HEWSON, R.; HOFFMANN, B.; HONGO, S.; HÖPER, D.; HORIE, M.; HUGHES, H. R.; HYNDMAN, T. H.; JAMBAI, A.; JARDIM, R.; JIANG, D.; JIN, Q.; JONSON, G. B.; JUNGLEN, S.; KARADAG, S.; KELLER, K. E.; KLEMPA, B.; KLINGSTRÖM, J.; KOBINGER, G.; KONDO, H.; KOONIN, E. V.; KRUPOVIC, M.; KURATH, G.; KUZMIN, I. V.; LAENEN, L.; LAMB, R. T A.; LAMBERT, A. J.; LANGEVIN, S. L.; LEE, B.; LEMOS, E. R. S.; LEROY, E. M.; LI, D.; Li, J.; LIANG, M.; LIÚ, W.; LIÚ, Y.; LUKASHEVICH, I. S.; MAES, P.; SOUZA, W. M. de; MARKLEWITZ, M.; MARSHALL, S. H.; MARTELLI, G. P.; MARTIN, R. R.; MARZANO, S.-L.; MASSART, S.; MCCAULEY, J. W.; MIELKE-EHRET, N.; MINAFRA, A.; MINUTOLO, M.; MIRAZIMI, A.; MÜHLBACH, H.-P.; MÜHLBERGER, E.; NAIDU, R.; NATSUAKI, T.; NAVARRO, B.; NAVARRO, J. A.; NETESOV, S. V.; NEUMANN, G.; NOWOTNY, N.; NUNES, M. R. T.; NYLUND, A.; ØKLAND, A. L.; OLIVEIRA, R. C.; PALACIOS, G.; PALLAS, V.; PÁLYI, B.; PAPA, A.; PARRISH, C. R.; PAUVOLID-CORRÊA, A.; PAWESKA, J. T.; PAYNE, S.; PÉREZ, D. R.; PFAFF, F.; RADOSHITZKY, S. R.; RAHMAN, A.; RAMOS-GONZÁLEZ, P. L.; RESENDE, R. O.; REYES, C. A.; RIMA, B. K.; ROMANOWSKI, VÍCTOR; LUNA, G. R.; ROTA, P.; RUBBENSTROTH, D.; RUNSTADLER, J. A.; RUZEK, D.; SABANADZOVIC, S.; SALÁT, J.; SALL, A. A.; SALVATO, M. S.; SARPKAYA, K.; SASAYA, T.; SCHWEMMLE, M.; SHABBIR, M. Z.; SHÍ, X.; SHÍ, Z.; SHIRAKO, Y.; SIMMONDS, P.; SIRMAROVÁ, J. |
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Afiliação: |
JENS H. KUHN; SCOTT ADKINS; DANIELA ALIOTO; SERGEY V. ALKHOVSKY; GAYA K. AMARASINGHE; TATJANA AVSIC-ZUPANC; MARÍA A. AYLLÓN; JUSTIN BAHL; ANNE BALKEMA-BUSCHMANN; MATTHEW J. BALLINGER; TOMÁS BARTONICKA; CHRISTOPHER BASLER; SINA BAVARI; MARTIN BEER; DENNIS A. BENTE; ÉRIC BERGERON; BRIAN H. BIRD; CAROL BLAIR; KIM R. BLASDELL; STEVEN B. BRADFUTE; RACHEL BREYTA; THOMAS BRIESE; PAUL A. BROWN; URSULA J. BUCHHOL; MICHAEL J. BUCHMEIER; ALEXANDER BUKREYEV; FELICITY BURT; NIHAL BUZKAN; CHARLES H. CALISHER; MENGJI CAO; INMACULADA CASAS; OHN CHAMBERLAIN; KARTIK CHANDRAN; RÉMI N. CHARREL; BIAO CHEN; MICHELA CHIUMENTI; IL-RYONG CHO; J. CHRISTOPHER S. CLEGG; IAN CROZIER; JOHN V. DA GRAÇA; ELENA DAL BÓ; ALBERTO M. R. DÁVILA; JUAN CARLOS DE LA TORRE; XAVIER DE LAMBALLERIE; RIK L. DE SWART; PATRICK L. DI BELLO; NICHOLAS DI PAOLA; FRANCESCO DI SERIO; RALF G. DIETZGEN; MICHELE DIGIARO; VALERIAN V. DOLJA; OLGA DOLNIK; MICHAEL A. DREBOT; JAN FELIX DREXLER; RALF DÜRRWALD; LUCIE DUFKOVA; WILLIAM G. DUNDON; W. PAUL DUPREX; JOHN M. DYE; ANDREW J. EASTON; HIDEKI EBIHARA; TOUFIC ELBEAINO; KORAY ERGÜNAY; JORLAN FERNANDES; ANTHONY R. FOOK; PIERRE B. H. FORMENT; LEONIE F. FORTH; RON A. M. FOUCHIER; JULIANA DE FREITAS ASTUA, CNPMF; SELMA GAGO-ZACHERT; GEORGE FÚ GÃO; MARÍA LAURA GARCÍA; ADOLFO GARCÍA-SASTRE; AURA R. GARRISON; AIAH GBAKIMA; TRACEY GOLDSTEIN; JEAN-PAUL J. GONZALEZ; ANTHONY GRIFFITHS; MARTIN H. GROSCHUP; STEPHAN GÜNTHER; ALEXANDRO GUTERRES; ROY A. HALL; JOHN HAMMOND; MOHAMED HASSAN; JUSSI HEPOJOKI; SATU HEPOJOKI; UDO HETZEL; ROGER HEWSON; BERND HOFFMANN; SEIJI HONGO; DIRK HÖPER; MASAYUKI HORIE; HOLLY R. HUGHES; TIMOTHY H. HYNDMAN; AMARA JAMBAI; RODRIGO JARDIM; DÀOHÓNG JIANG; QI JIN; GILDA B. JONSON; SANDRA JUNGLEN; SERPIL KARADAG; KAREN E. KELLER; BORIS KLEMPA; JONAS KLINGSTRÖM; GARY KOBINGER; HIDEKI KONDO; EUGENE V. KOONIN; MART KRUPOVIC; GAEL KURATH; IVAN V. KUZMIN; LIES LAENEN; ROBERT A. LAMB; AMY J. LAMBERT; STANLEY L. LANGEVIN; BENHUR LEE; ELBA R. S. LEMOS; ERIC M. LEROY; DEXIN LI; JIÀNRÓNG Li; MIFANG LIANG; WÉNWÉN LIÚ; YÀN LIÚ; IGOR S. LUKASHEVICH; PIET MAES; WILLIAM MARCIEL DE SOUZA; MARCO MARKLEWITZ; SERGIO H. MARSHALL; GIOVANNI P. MARTELLI; ROBERT R. MARTIN; SHIN-YI L. MARZANO; SÉBASTIEN MASSART; JOHN W. MCCAULEY; NICOLE MIELKE-EHRET; ANGELANTONIO MINAFRA; MARIA MINUTOLO; ALI MIRAZIMI; HANS-PETER MÜHLBACH; ELKE MÜHLBERGER; RAYAPATI NAIDU; TOMOHIDE NATSUAKI; BEATRIZ NAVARRO; JOSÉ A. NAVARRO; SERGEY V. NETESOV; GABRIELE NEUMANN; NORBERT NOWOTNY; MÁRCIO R. T. NUNES; ARE NYLUND; ARNFINN L. ØKLAND; RENATA C. OLIVEIRA; GUSTAVO PALACIOS; VICENTE PALLAS; BERNADETT PÁLYI; ANNA PAPA; COLIN R. PARRISH; ALEX PAUVOLID-CORRÊA; JANUSZ T. PAWESKA; SUSAN PAYNE; DANIEL R. PÉREZ; FLORIAN PFAFF; SHELI R. RADOSHITZKY; AZIZ?UL RAHMAN; PEDRO L. RAMOS-GONZÁLEZ; RENATO O. RESENDE; CARINA A. REYES; BERTUS K. RIMA; VÍCTOR ROMANOWSKI; GABRIEL ROBLES LUNA; PAUL ROTA; DENNIS RUBBENSTROTH; JONATHAN A. RUNSTADLER; DANIEL RUZEK; SEAD SABANADZOVIC; JIRI SALÁT; AMADOU ALPHA SALL; MARIA S. SALVATO; KAMIL SARPKAYA; TAKAHIDE SASAYA; MARTIN SCHWEMMLE; MUHAMMAD Z. SHABBIR; XIAOHÓNG SHÍ; ZHÈNGLÌ SHÍ; YUKIO SHIRAKO; PETER SIMMONDS; JANA SIRMAROVÁ. |
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Título: |
2020 taxonomic update for phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Archives of Virology, September, 2020. |
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ISSN: |
0304-8608 |
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Idioma: |
Inglês |
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Thesagro: |
Doença de Planta. |
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Categoria do assunto: |
-- |
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Marc: |
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700 1 $aSIRMAROVÁ, J.
773 $tArchives of Virology, September, 2020.
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Embrapa Mandioca e Fruticultura (CNPMF) |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
25/11/2019 |
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Data da última atualização: |
23/08/2020 |
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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: |
DIAS, K. O. G.; PIEPHO, H. P.; GUIMARAES, L. J. M.; GUIMARAES, P. E. de O.; PARENTONI, S. N.; PINTO, M. de O.; NODA, R. W.; MAGALHAES, J. V. de; GUIMARÃES, C. T.; GARCIA, A. A. F.; PASTINA, M. M. |
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Afiliação: |
Escola Superior de Agricultura Luiz de Queiroz; University of Hohenheim; LAURO JOSE MOREIRA GUIMARAES, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; SIDNEY NETTO PARENTONI, CNPMS; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; JURANDIR VIEIRA DE MAGALHAES, CNPMS; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Escola Superior de Agricultura Luiz de Queiroz; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 133, p. 443-455, 2020. |
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DOI: |
10.1007/s00122-019-03475-1 |
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Idioma: |
Inglês |
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Notas: |
Publicado online em 22 nov. 2019. |
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Conteúdo: |
Predicting the performance of untested single-cross hybrids through genomic prediction (GP) is highly desirable to increase genetic gain. Here, we evaluate the predictive ability (PA) of novel genomic strategies to predict single-cross maize hybrids using an unbalanced historical dataset of a tropical breeding program. Field data comprised 949 single-cross hybrids evaluated from 2006 to 2013, representing eight breeding cycles. Hybrid genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GP analyses were fitted using genomic best linear unbiased prediction via a stage-wise approach, considering two distinct cross-validation schemes. Results highlight the importance of taking into account the uncertainty regarding the adjusted means at each step of a stage-wise analysis, due to the highly unbalanced data structure and the expected heterogeneity of variances across years and locations of a commercial breeding program. Further, an increase in the size of the training set was not always advantageous even in the same breeding program. The use of the two cycles preceding predictions achieved optimal PA of untested single-cross hybrids in a forward prediction scenario, which could be used to replace the first step of field screening. Finally, in addition to the practical and theoretical results applied to maize hybrid breeding programs, the stage-wise analysis performed in this study may be applied to any crop historical unbalanced data. MenosPredicting the performance of untested single-cross hybrids through genomic prediction (GP) is highly desirable to increase genetic gain. Here, we evaluate the predictive ability (PA) of novel genomic strategies to predict single-cross maize hybrids using an unbalanced historical dataset of a tropical breeding program. Field data comprised 949 single-cross hybrids evaluated from 2006 to 2013, representing eight breeding cycles. Hybrid genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GP analyses were fitted using genomic best linear unbiased prediction via a stage-wise approach, considering two distinct cross-validation schemes. Results highlight the importance of taking into account the uncertainty regarding the adjusted means at each step of a stage-wise analysis, due to the highly unbalanced data structure and the expected heterogeneity of variances across years and locations of a commercial breeding program. Further, an increase in the size of the training set was not always advantageous even in the same breeding program. The use of the two cycles preceding predictions achieved optimal PA of untested single-cross hybrids in a forward prediction scenario, which could be used to replace the first step of field screening. Finally, in addition to the practical and theoretical results applied to maize hybrid breeding programs, the stage-wise analysis performed in this st... Mostrar Tudo |
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Thesagro: |
Genoma; Hibrido; Melhoramento 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/215380/1/Novel-strategies.pdf
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Marc: |
LEADER 02512naa a2200313 a 4500
001 2115056
005 2020-08-23
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1007/s00122-019-03475-1$2DOI
100 1 $aDIAS, K. O. G.
245 $aNovel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.$h[electronic resource]
260 $c2020
500 $aPublicado online em 22 nov. 2019.
520 $aPredicting the performance of untested single-cross hybrids through genomic prediction (GP) is highly desirable to increase genetic gain. Here, we evaluate the predictive ability (PA) of novel genomic strategies to predict single-cross maize hybrids using an unbalanced historical dataset of a tropical breeding program. Field data comprised 949 single-cross hybrids evaluated from 2006 to 2013, representing eight breeding cycles. Hybrid genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GP analyses were fitted using genomic best linear unbiased prediction via a stage-wise approach, considering two distinct cross-validation schemes. Results highlight the importance of taking into account the uncertainty regarding the adjusted means at each step of a stage-wise analysis, due to the highly unbalanced data structure and the expected heterogeneity of variances across years and locations of a commercial breeding program. Further, an increase in the size of the training set was not always advantageous even in the same breeding program. The use of the two cycles preceding predictions achieved optimal PA of untested single-cross hybrids in a forward prediction scenario, which could be used to replace the first step of field screening. Finally, in addition to the practical and theoretical results applied to maize hybrid breeding programs, the stage-wise analysis performed in this study may be applied to any crop historical unbalanced data.
650 $aGenoma
650 $aHibrido
650 $aMelhoramento Vegetal
650 $aMilho
700 1 $aPIEPHO, H. P.
700 1 $aGUIMARAES, L. J. M.
700 1 $aGUIMARAES, P. E. de O.
700 1 $aPARENTONI, S. N.
700 1 $aPINTO, M. de O.
700 1 $aNODA, R. W.
700 1 $aMAGALHAES, J. V. de
700 1 $aGUIMARÃES, C. T.
700 1 $aGARCIA, A. A. F.
700 1 $aPASTINA, M. M.
773 $tTheoretical and Applied Genetics$gv. 133, p. 443-455, 2020.
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Embrapa Milho e Sorgo (CNPMS) |
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