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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
16/03/2018 |
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Autor : |
WILLIAMS, E.R.; MATHESON, A.C. |
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Afiliación : |
EMLYN RHYS WILLIAMS; ALASTAIR COLIN MATHESON. |
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Título : |
Experimental design and analysis for use in tree improvement. |
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Fecha de publicación : |
1994 |
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Fuente / Imprenta : |
East Melbourne, Victoria (Australia): CSIRO, 1994. |
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Páginas : |
174 p. |
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ISBN : |
0-643-05555-X |
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Idioma : |
Inglés |
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Notas : |
Bibliografía: p. 167-169 |
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Palabras claves : |
BIOMETRY; FORESTS AND FORESTRY. |
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Thesagro : |
ARBOLES FORESTALES; BIOMETRIA; EXPERIMENTACION; FITOMEJORAMIENTO; METODOS ESTADISTICOS. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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Marc : |
LEADER 00648nam a2200229 a 4500
001 1017436
005 2018-03-16
008 1994 bl uuuu u00u1 u #d
020 $a0-643-05555-X
100 1 $aWILLIAMS, E.R.
245 $aExperimental design and analysis for use in tree improvement.
260 $aEast Melbourne, Victoria (Australia): CSIRO$c1994
300 $a174 p.
500 $aBibliografía: p. 167-169
650 $aARBOLES FORESTALES
650 $aBIOMETRIA
650 $aEXPERIMENTACION
650 $aFITOMEJORAMIENTO
650 $aMETODOS ESTADISTICOS
653 $aBIOMETRY
653 $aFORESTS AND FORESTRY
700 1 $aMATHESON, A.C.
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Registro original : |
INIA Tacuarembó (TBO) |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
13/09/2021 |
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Actualizado : |
14/09/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
-- - -- |
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Autor : |
CRUZ, M.; ARBELAEZ, J. D.; LOAIZA, K.; CUASQUER, J.; ROSAS, J.E.; GRATEROL, E. |
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Afiliación : |
MARIBEL CRUZ, FLAR (Fondo Latinoamericano para Arroz de Riego); CIAT (International Center for Tropical Agriculture), Colombia.; JUAN DAVID ARBELAEZ, Dep. of Crop Sciences, Univ. of Illinois, USA.; KATHERINE LOAIZA, FLAR (Fondo Latinoamericano para Arroz de Riego) ; CIAT (International Center for Tropical Agriculture), Colombia.; JUAN CUASQUER, CIAT (International Center for Tropical Agriculture), Colombia.; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EDUARDO GRATEROL, FLAR (Fondo Latinoamericano para Arroz de Riego); CIAT (International Center for Tropical Agriculture), Colombia. |
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Título : |
Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
The Plant Genome, September, 2021 OPEN ACCESS, e20134. Doi: https://doi.org/10.1002/tpg2.20134 |
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Páginas : |
16 p. |
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DOI : |
10.1002/tpg2.20134 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 24 February 2021; Accepted: 23 June 2021; Corresponding author Juan David Arbelaez arbelaez@illinois.edu |
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Contenido : |
Abstract
Rice (Oryza sativa L.)grain quality is a set of complex interrelated traits that include grain milling, appearance, cooking, and edible properties. As consumer preferences in Latin America and the Caribbean evolve, determining what traits best capture regional grain quality preferences is fundamental for breeding and cultivar release.
In this study, a genome-wide association study (GWAS), marker-assisted selection (MAS), and genomic selection (GS) were evaluated to help guide the development of new breeding strategies for rice grain quality improvement. For this purpose, 284 rice lines representing over 20 yr of breeding in Latin America and the Caribbean were genotyped and phenotyped for 10 different traits including grain milling, appearance, cooking, and edible quality traits. Genetic correlations among the 10 traits ranged from ?0.83 to 0.85. A GWAS identified 19 significant marker/trait combinations associated with eight grain quality traits. Four functional markers, three located in the Waxy and one in the starch synthase IIa genes, were significantly associated with six grain-quality traits. These markers individually explained 51?75% of the phenotypic variance depending on the trait, clearly indicating their potential utility for MAS.
Cross-validation studies to evaluate predictive abilities of four different GS models for each of the 10 quality traits were conducted and predictive abilities ranged from 0.3 to 0.72. Overall, the machine learning model random forest had the highest predictive abilities and was especially effective for traits where large effect quantitative trait loci were identified. This study provides the foundation for deploying effective molecular breeding strategies for grain quality in Latin American rice breeding programs. MenosAbstract
Rice (Oryza sativa L.)grain quality is a set of complex interrelated traits that include grain milling, appearance, cooking, and edible properties. As consumer preferences in Latin America and the Caribbean evolve, determining what traits best capture regional grain quality preferences is fundamental for breeding and cultivar release.
In this study, a genome-wide association study (GWAS), marker-assisted selection (MAS), and genomic selection (GS) were evaluated to help guide the development of new breeding strategies for rice grain quality improvement. For this purpose, 284 rice lines representing over 20 yr of breeding in Latin America and the Caribbean were genotyped and phenotyped for 10 different traits including grain milling, appearance, cooking, and edible quality traits. Genetic correlations among the 10 traits ranged from ?0.83 to 0.85. A GWAS identified 19 significant marker/trait combinations associated with eight grain quality traits. Four functional markers, three located in the Waxy and one in the starch synthase IIa genes, were significantly associated with six grain-quality traits. These markers individually explained 51?75% of the phenotypic variance depending on the trait, clearly indicating their potential utility for MAS.
Cross-validation studies to evaluate predictive abilities of four different GS models for each of the 10 quality traits were conducted and predictive abilities ranged from 0.3 to 0.72. Overall, the machine learning model random... Presentar Todo |
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Palabras claves : |
CALIDAD CULINARIA; CALIDAD DE GRANO; CALIDAD DE LA SEMILLA; 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/15974/1/The-Plant-Genome-2021-Cruz.pdf
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Marc : |
LEADER 02841naa a2200277 a 4500
001 1062397
005 2021-09-14
008 2021 bl uuuu u00u1 u #d
024 7 $a10.1002/tpg2.20134$2DOI
100 1 $aCRUZ, M.
245 $aGenetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean.$h[electronic resource]
260 $c2021
300 $a16 p.
500 $aArticle history: Received: 24 February 2021; Accepted: 23 June 2021; Corresponding author Juan David Arbelaez arbelaez@illinois.edu
520 $aAbstract Rice (Oryza sativa L.)grain quality is a set of complex interrelated traits that include grain milling, appearance, cooking, and edible properties. As consumer preferences in Latin America and the Caribbean evolve, determining what traits best capture regional grain quality preferences is fundamental for breeding and cultivar release. In this study, a genome-wide association study (GWAS), marker-assisted selection (MAS), and genomic selection (GS) were evaluated to help guide the development of new breeding strategies for rice grain quality improvement. For this purpose, 284 rice lines representing over 20 yr of breeding in Latin America and the Caribbean were genotyped and phenotyped for 10 different traits including grain milling, appearance, cooking, and edible quality traits. Genetic correlations among the 10 traits ranged from ?0.83 to 0.85. A GWAS identified 19 significant marker/trait combinations associated with eight grain quality traits. Four functional markers, three located in the Waxy and one in the starch synthase IIa genes, were significantly associated with six grain-quality traits. These markers individually explained 51?75% of the phenotypic variance depending on the trait, clearly indicating their potential utility for MAS. Cross-validation studies to evaluate predictive abilities of four different GS models for each of the 10 quality traits were conducted and predictive abilities ranged from 0.3 to 0.72. Overall, the machine learning model random forest had the highest predictive abilities and was especially effective for traits where large effect quantitative trait loci were identified. This study provides the foundation for deploying effective molecular breeding strategies for grain quality in Latin American rice breeding programs.
653 $aCALIDAD CULINARIA
653 $aCALIDAD DE GRANO
653 $aCALIDAD DE LA SEMILLA
653 $aFITOMEJORAMIENTO
653 $aRICE
700 1 $aARBELAEZ, J. D.
700 1 $aLOAIZA, K.
700 1 $aCUASQUER, J.
700 1 $aROSAS, J.E.
700 1 $aGRATEROL, E.
773 $tThe Plant Genome, September, 2021 OPEN ACCESS, e20134. Doi: https://doi.org/10.1002/tpg2.20134
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