| |
|
|
| Registros recuperados : 46 | |
| 1. |  | Francis, M.Bonnecarrère, V. La biotecnología en Uruguay ln: Carneiro, M., coord. Estrategias de biotecnología agropecuaria para el Cono Sur. Montevideo (Uruguay): PROCISUR, 2001. p. 93-129 (PROCISUR Documentos)| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó; INIA Treinta y Tres. |
|   |
| 6. | | BONNECARRERE, V.; IRIARTE, W.; SALDAIN, N.E. La resistencia que expresa el biotipo de capín E3CL al Kifix. ¿A qué mecanismos es debida? ln: JORNADA ANUAL ARROZ, 2016, INIA TREINTA Y TRES, TREINTA Y TRES, UY. Arroz: resultados experimentales 2015-2016. Treinta y Tres, (Uruguay): INIA, 2016. cap. 2, p. 18-20. (INIA Serie Actividades de Difusión; 765)| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
|  |
| 18. | | DIEZ VIGNOLA, M.; SAINZ, M.; SALDAIN, N.E.; MARCHESI, C.; BONNECARRERE, V.; DÍAZ GADEA, P. Limited induction of ethylene and cyanide synthesis are observed in quinclorac-resistant barnyardgrass (Echinochloa crus-galli) in Uruguay. Weed Science, 1 July 2020, Volume 68, Issue 4, Pages 348-357. Doi: https://doi.org/10.1017/wsc.2020.32 Article history: Article accepted and Published online by Cambridge University Press: 28 April 2020| Biblioteca(s): INIA Treinta y Tres. |
|  |
| Registros recuperados : 46 | |
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Treinta y Tres. |
|
Fecha actual : |
29/05/2019 |
|
Actualizado : |
11/10/2019 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Circulación / Nivel : |
-- - -- |
|
Autor : |
MONTEVERDE, E.; GUTIERREZ, L.; BLANCO, P.H.; PÉREZ DE VIDA, F.; ROSAS, J.E.; BONNECARRERE, V.; QUERO, G.; MCCOUCH, SUSAN |
|
Afiliación : |
ELIANA MONTEVERDE, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University.; LUCÍA GUTIERREZ, Department of Agronomy, University of Wisconsin.; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University. |
|
Título : |
Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in subtropical areas. |
|
Fecha de publicación : |
2019 |
|
Fuente / Imprenta : |
G3: GENES, GENOMES, GENETICS May 1, 2019, v.9 (5), p. 1519-1531. OPEN ACCESS. |
|
DOI : |
10.1534/g3.119.400064 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: Manuscript received February 6, 2019 // Accepted for publication March 5, 2019// Published Early Online March 15, 2019.
Supplemental material available at Figshare: https://doi.org/10.25387/g3.7685636 |
|
Contenido : |
Understanding the genetic and environmental basis of genotype · environment interaction (G·E) is of fundamental importance in plant breeding. If we consider G·E in the context of genotype · year interactions (G·Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice (Oryza sativa L.) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. In this study, we used molecular marker data and environmental covariates (EC) simultaneously to predict rice yield, milling quality traits and plant height in untested environments (years), using both reaction norm models and partial least squares (PLS), in two rice breeding populations (indica and tropical japonica). We also sought to explain G·E by differential quantitative trait loci (QTL) expression in relation to EC. Our results showed that PLS models trained with both molecular markers and EC gave better prediction accuracies than reaction norm models when predicting future years. We also detected milling quality QTL that showed a differential expression conditional on humidity and solar radiation, providing insight for the main environmental factors affecting milling quality in subtropical and temperate rice growing areas. |
|
Palabras claves : |
ENVIRONMENTAL COVARIATES; GENOMIC PREDICTIONS; GENOTYPE BY ENVIRONMENT INTERACTION; QTL BY ENVIRONMENT INTERACTION. |
|
Thesagro : |
ARROZ; FITOMEJORAMIENTO; RICE. |
|
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
|
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12705/1/Blanco-Genes-Genomes-Genetics-2019.pdf
|
|
Marc : |
LEADER 02649naa a2200313 a 4500
001 1059786
005 2019-10-11
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1534/g3.119.400064$2DOI
100 1 $aMONTEVERDE, E.
245 $aIntegrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in subtropical areas.$h[electronic resource]
260 $c2019
500 $aArticle history: Manuscript received February 6, 2019 // Accepted for publication March 5, 2019// Published Early Online March 15, 2019. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7685636
520 $aUnderstanding the genetic and environmental basis of genotype · environment interaction (G·E) is of fundamental importance in plant breeding. If we consider G·E in the context of genotype · year interactions (G·Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice (Oryza sativa L.) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. In this study, we used molecular marker data and environmental covariates (EC) simultaneously to predict rice yield, milling quality traits and plant height in untested environments (years), using both reaction norm models and partial least squares (PLS), in two rice breeding populations (indica and tropical japonica). We also sought to explain G·E by differential quantitative trait loci (QTL) expression in relation to EC. Our results showed that PLS models trained with both molecular markers and EC gave better prediction accuracies than reaction norm models when predicting future years. We also detected milling quality QTL that showed a differential expression conditional on humidity and solar radiation, providing insight for the main environmental factors affecting milling quality in subtropical and temperate rice growing areas.
650 $aARROZ
650 $aFITOMEJORAMIENTO
650 $aRICE
653 $aENVIRONMENTAL COVARIATES
653 $aGENOMIC PREDICTIONS
653 $aGENOTYPE BY ENVIRONMENT INTERACTION
653 $aQTL BY ENVIRONMENT INTERACTION
700 1 $aGUTIERREZ, L.
700 1 $aBLANCO, P.H.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aROSAS, J.E.
700 1 $aBONNECARRERE, V.
700 1 $aQUERO, G.
700 1 $aMCCOUCH, SUSAN
773 $tG3: GENES, GENOMES, GENETICS May 1, 2019$gv.9 (5), p. 1519-1531. OPEN ACCESS.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Treinta y Tres (TT) |
|
|
Embrapa
Todos los derechos reservados, conforme Ley n° 9.610
Política de Privacidad
Área Restricta
|
|
Instituto Nacional de Investigación Agropecuaria
Andes 1365 - piso 12 CP 11100 Montevideo, Uruguay
Tel: +598 2902 0550 Fax: +598 2902 3666
bibliotecas@inia.org.uy
|
|
|
|
|
