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| 1623. |  | MARCHESI, C. Resumen de factores calimáticos en la zona Norte del país. In: INIA Tacuarembó. Programa Nacional de Investigación en Producción de Arroz. Presentación resultados experimentales de arroz: Zafra 2012-2013, 6-7 agosto, Tacuarembó, Artigas, Uruguay. Tacuarembó (Uruguay): INIA, 2013. p. 5-14 (INIA Serie Actividades de Difusión ; 715)| Biblioteca(s): INIA Tacuarembó. |
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| 1626. | | GIBBONS, J.W.; MOLDENHAUER, K.A.K.; LEE, F.N.; BERNHARDT, J.L.; ANDERS, M.; SLATON, N.A.; NORMAN, R.J.; DILDAY, R.H.; RUTGER, J.N.; BLOCKER, M.M.; BULLOCH, J.M.; TOLBERT, A.C. Rice breeding and genetics: Development of semi-dwarf long and medium-grain cultivars. ln: Norman, R.J.; Beyrouty, C.A. B.R. Wells Rice research studies 1999. Fayetteville, Arkansas (USA): Arkansas Agricultural Experiment Station, 2000. p. 28-35 (AAES Research Series ; 476)| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 1634. | | COUNCE, P.A.; BRYANT, R.J.; BAUTISTA, R.C.; BERGMAN, C.J.; WANG, Y.J.; SIEBENMORGEN, T.J.; MOLDENHAUER, K.A.K.; MEULLENET, J.F. Rice milling quality and starch branching as affected by high night temperatures. ln: Norman, R.J.; Meullenet, J.F. B.R. Wells Rice research studies 2000. Fayetteville, Arkansas (USA): Arkansas Agricultural Experiment Station, 2001. p. 259-263 (AAES Research Series ; 485)| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 1638. | | CARRACELAS, G.; FERRES, S.; CAL, A. Riego con mangs en chacras comerciales. In: Día de Campo, arroz, 7 de febrero, Zona Norte, Paso Farías, Artigas / 8 de febrero, Zona Centro, Pueblo del Barro, Tacuarembó, 2017. Tacuarembó (Uruguay): INIA, 2017. p. 61-66| Biblioteca(s): INIA Tacuarembó. |
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| 1639. | | DEAMBROSI, E.; ZORRILLA DE SAN MARTÍN, G.; LAUZ, M.; BLANCO, P.H.; TERRA, J.A. Rompiendo el techo de rendimiento del cultivo de arroz. Proyecto ANII ALI_1_2012_1_3507 (INIA, GMA-COOPAR, ACA) Zafra 2016-2017 - Trabajos de validación. In: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017. p. 89-93. (INIA Serie Técnica; 233)| Biblioteca(s): INIA Treinta y Tres. |
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| Registros recuperados : 2,954 | |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
29/05/2019 |
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Actualizado : |
11/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
-- - -- |
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Autor : |
MONTEVERDE, E.; GUTIERREZ, L.; BLANCO, P.H.; PÉREZ DE VIDA, F.; ROSAS, J.E.; BONNECARRERE, V.; QUERO, G.; MCCOUCH, SUSAN |
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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. |
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Título : |
Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in subtropical areas. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
G3: GENES, GENOMES, GENETICS May 1, 2019, v.9 (5), p. 1519-1531. OPEN ACCESS. |
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DOI : |
10.1534/g3.119.400064 |
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Idioma : |
Inglés |
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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 |
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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. |
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Palabras claves : |
ENVIRONMENTAL COVARIATES; GENOMIC PREDICTIONS; GENOTYPE BY ENVIRONMENT INTERACTION; QTL BY ENVIRONMENT INTERACTION. |
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Thesagro : |
ARROZ; FITOMEJORAMIENTO; RICE. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12705/1/Blanco-Genes-Genomes-Genetics-2019.pdf
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|
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.
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