Fecha/hora: 03/06/2024 20:15:42

Biblioteca(s): | INIA Treinta y Tres.
Fecha corriente: | 29/05/2019
Fecha de la última actualización: | 11/10/2019
Autoria: | 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.
Año 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-Clave: | ENVIRONMENTAL COVARIATES; GENOMIC PREDICTIONS; GENOTYPE BY ENVIRONMENT INTERACTION; QTL BY ENVIRONMENT INTERACTION.
Thesagro: | ARROZ; FITOMEJORAMIENTO; RICE.
Categoría del tema: | F30 Genética vegetal y fitomejoramiento
URL: | http://www.ainfo.inia.uy/digital/bitstream/item/12705/1/Blanco-Genes-Genomes-Genetics-2019.pdf
Registro original: | INIA Treinta y Tres (TT)
Ejemplares: | TT - (102762-1) - PXI - AP - DD - PP/3G.GENES-GENOMES-GENETICS/2019 -  - 3g-2019 - DIS