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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
12/08/2016 |
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Actualizado : |
02/01/2017 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO, Universidad de la República (UdelaR)/ Facultad de Agronomía; PABLO GONZÁLEZ BARRIOS, Universidad de la República (UdelaR)/ Facultad de Agronomía; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCIA GUTIÉRREZ, Universidad de la República (UdelaR)/ Facultad de Agronomía. |
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Título : |
Modeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Crop Science, 2016, v. 56, p. 1-15. OPEN ACCESS. |
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DOI : |
http://dx.doi.org/10.2135/cropsci2015.04.0207 |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT.
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.
© 2016. Crop Science Society of America, Inc. MenosABSTRACT.
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ab... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; WHEAT. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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URL : |
http://dx.doi.org/10.2135/cropsci2015.04.0207
http://www.ainfo.inia.uy/digital/bitstream/item/5875/1/Lado-B.-2016.-Crop-Science.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/5876/1/Lado-B.-2016.-Crop-Science-supplement.pdf
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Marc : |
LEADER 02297naa a2200217 a 4500
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008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.2135/cropsci2015.04.0207$2DOI
100 1 $aLADO, B.
245 $aModeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program.$h[electronic resource]
260 $c2016
520 $aABSTRACT. Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context. © 2016. Crop Science Society of America, Inc.
650 $aTRIGO
653 $aGENOMIC SELECTION
653 $aWHEAT
700 1 $aGONZÁLEZ BARRIOS, P.
700 1 $aQUINCKE, M.
700 1 $aSILVA, P.
700 1 $aGUTIÉRREZ, L.
773 $tCrop Science, 2016$gv. 56, p. 1-15. OPEN ACCESS.
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Registro original : |
INIA Las Brujas (LB) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Tacuarembó. |
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Fecha actual : |
21/10/2014 |
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Actualizado : |
25/11/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 2 |
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Autor : |
REINA, L.; GALETTA, A.; VINCIGUERRA, V.; RESQUIN, F.; MENÉNDEZ, P. |
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Afiliación : |
JOSE FERNANDO RESQUIN PEREZ, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
The relationship between Eucalyptus grandis lignin structure and kraft pulping parameters. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Journal of Analytical and Applied Pyrolysis, 2014, v.107, p.284-288. |
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ISSN : |
0165-2370 |
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DOI : |
10.1016/j.jaap.2014.03.013 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 10 December 2013 / Accepted 27 March 2014 / Available online 4 April 2014. |
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Contenido : |
ABSTRACT.
The syringyl/guaiacyl (S/G) ratio of Eucalyputs grandis lignin was determined and its relation with kraft pulping parameters was studied. Twenty one wood samples obtained from 10-year-old trees grown in the same place were analyzed using Py-GC-MS to determine the syringyl/guaiacyl (S/G) ratio. The samples were pulped to the same final lignin content (Kappa number 18) obtaining pulp yields between 48.8% and 54.3%. Relationships were observed between pulp yield and S/G ratio (r = 0.51) also between alkali charge used in pulping and S/G ratio (r = -0.60).
© 2014 Elsevier B.V. |
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Palabras claves : |
FOREST AND FORESTRY; FORESTACIÓN. |
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Thesagro : |
CONTENIDO DE LIGNINA; CROMATOGRAFÍA DE GASES; EUCALYPTUS GRANDIS. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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Marc : |
LEADER 01479naa a2200265 a 4500
001 1051213
005 2019-11-25
008 2014 bl uuuu u00u1 u #d
022 $a0165-2370
024 7 $a10.1016/j.jaap.2014.03.013$2DOI
100 1 $aREINA, L.
245 $aThe relationship between Eucalyptus grandis lignin structure and kraft pulping parameters.$h[electronic resource]
260 $c2014
500 $aArticle history: Received 10 December 2013 / Accepted 27 March 2014 / Available online 4 April 2014.
520 $aABSTRACT. The syringyl/guaiacyl (S/G) ratio of Eucalyputs grandis lignin was determined and its relation with kraft pulping parameters was studied. Twenty one wood samples obtained from 10-year-old trees grown in the same place were analyzed using Py-GC-MS to determine the syringyl/guaiacyl (S/G) ratio. The samples were pulped to the same final lignin content (Kappa number 18) obtaining pulp yields between 48.8% and 54.3%. Relationships were observed between pulp yield and S/G ratio (r = 0.51) also between alkali charge used in pulping and S/G ratio (r = -0.60). © 2014 Elsevier B.V.
650 $aCONTENIDO DE LIGNINA
650 $aCROMATOGRAFÍA DE GASES
650 $aEUCALYPTUS GRANDIS
653 $aFOREST AND FORESTRY
653 $aFORESTACIÓN
700 1 $aGALETTA, A.
700 1 $aVINCIGUERRA, V.
700 1 $aRESQUIN, F.
700 1 $aMENÉNDEZ, P.
773 $tJournal of Analytical and Applied Pyrolysis, 2014$gv.107, p.284-288.
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