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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
26/09/2014 |
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Actualizado : |
06/11/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LADO, B.; MATUS, I.; RODRIGUEZ, A.; INOSTROZA, L.; POLAND, J.; BELZILE ,F.; DEL POZO, A.; QUINCKE, M.; CASTRO, M.; VON ZITZEWITZ, J. |
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Afiliación : |
BETTINA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JARISLAV RAMON VON ZITZEWITZ VON SALVIATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. |
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Fecha de publicación : |
2013 |
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Fuente / Imprenta : |
G3: Genes, Genomes, Genetics (Bethesda), v. 3, n,12, p. 2105-2114, 2013.OPEN ACCESS. |
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ISSN : |
2160-1836. |
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DOI : |
10.1534/g3.113.007807 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 2013 Aug 26 // Accepted 2013 Sep 18. |
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Contenido : |
Abstract:
In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models. MenosAbstract:
In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confir... Presentar Todo |
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Palabras claves : |
GBLUP; GENOMIC SELECTION; GENOTIPADO POR SECUENCIACIÓN; GENOTYPING BY SEQUENCING; GENPRED; LOCUS DE UN CARÁCTER CUANTITATIVO; MEJOR PREDICTOR LINEAR INSESGADO; POLIMORFISMO DE NUCLEÓTICO SIMPLE; QTL; QUANTITATIVE TRAIT LOCUS; SELECCIÓN GENÓMICA; SHARED DATA RESOURCES; SINGLE NUCLEOTIDE POLYMORPHISM; SPATIAL CORRECTION; WHEAT. |
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Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13756/1/G3Bethesda-v.-3-n12-p.-2105-2114-2013.pdf
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Marc : |
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008 2013 bl uuuu u00u1 u #d
022 $a2160-1836.
024 7 $a10.1534/g3.113.007807$2DOI
100 1 $aLADO, B.
245 $aIncreased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.$h[electronic resource]
260 $c2013
500 $aArticle history: Received 2013 Aug 26 // Accepted 2013 Sep 18.
520 $aAbstract: In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.
650 $aTRIGO
650 $aTRITICUM AESTIVUM
653 $aGBLUP
653 $aGENOMIC SELECTION
653 $aGENOTIPADO POR SECUENCIACIÓN
653 $aGENOTYPING BY SEQUENCING
653 $aGENPRED
653 $aLOCUS DE UN CARÁCTER CUANTITATIVO
653 $aMEJOR PREDICTOR LINEAR INSESGADO
653 $aPOLIMORFISMO DE NUCLEÓTICO SIMPLE
653 $aQTL
653 $aQUANTITATIVE TRAIT LOCUS
653 $aSELECCIÓN GENÓMICA
653 $aSHARED DATA RESOURCES
653 $aSINGLE NUCLEOTIDE POLYMORPHISM
653 $aSPATIAL CORRECTION
653 $aWHEAT
700 1 $aMATUS, I.
700 1 $aRODRIGUEZ, A.
700 1 $aINOSTROZA, L.
700 1 $aPOLAND, J.
700 1 $aBELZILE ,F.
700 1 $aDEL POZO, A.
700 1 $aQUINCKE, M.
700 1 $aCASTRO, M.
700 1 $aVON ZITZEWITZ, J.
773 $tG3: Genes, Genomes, Genetics (Bethesda)$gv. 3, n,12, p. 2105-2114, 2013.OPEN ACCESS.
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Registro original : |
INIA La Estanzuela (LE) |
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| Registros recuperados : 86 | |
| 62. |  | MACEDO, I.; CASTILLO, J.; SALDAIN, N.E.; MARTÍNEZ, S.; BORDAGORRI, P.; HERNANDEZ, J.; TERRA, J.A. Cerrando un ciclo de las rotaciones arroceras: rendimiento del cultivo de arroz y cultivos alternativos. In: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017. p. 63-65. (INIA Serie Técnica; 233)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 65. | | FABINI, G.; GRAHMANN, K.; CASTILLO, J.; GONZÁLEZ, P.; TERRA, J.A. Eficiencia de uso de N en el arroz: pensando en sistemas. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B. (Eds.) Arroz 2021. Montevideo (UY): INIA, 2022. p. 41-44. (INIA Serie Técnica; 262)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 69. | | CASTILLO, J.; KIRK, G. J. D.; JORDANA RIVERO, M.; DOBERMANN, A. The nitrogen economy of rice-livestock systems in Uruguay. Global Food Security, September 2021, Volume 30, Article number 100566, pages 1-8. DOI: https://doi.org/10.1016/j.gfs.2021.100566 Article history: Received 13 March 2021, Revised 29 July 2021, Accepted 30 July 2021, Available online 11 August 2021.
E-mail address: jcastillo@inia.org.uy| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : -- - -- |
| Biblioteca(s): INIA Treinta y Tres. |
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| 70. | | MÉNDEZ, R.; DEAMBROSI, E.; CASTILLO, J.; MARTINEZ, S.; SOSA, B.; CASALES, L. Respuesta del cultivar Parao a densidades de siembra y dosis de nitrógeno en tres ambientes. In: PROGRAMA NACIONAL PRODUCCIÓN DE ARROZ; JORNADA ANUAL ARROZ-SOJA, 2013, INIA TREINTA Y TRES, UY. Arroz-soja: resultados experimentales 2012-2013. Treinta y Tres: INIA, 2013. cap. 3, p. 1-3 (INIA Serie Actividades de Difusión; 713)| Biblioteca(s): INIA Treinta y Tres. |
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| 71. | | MÉNDEZ, R.; DEAMBROSI, E.; CASTILLO, J.; LAGO, F.; LAUZ, A.; MAGALLANES, A. Respuesta de Parao a densidades de siembra y dosis de nitrógeno. In: PROGRAMA NACIONAL PRODUCCIÓN DE ARROZ; JORNADA ANUAL ARROZ, 2012, INIA TREINTA Y TRES, UY. Arroz: resultados experimentales 2011-2012. Treinta y Tres: INIA, 2012. Cap. 3 p.20-24 (INIA Serie Actividades de Difusión; 686)| Biblioteca(s): INIA Treinta y Tres. |
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| 73. | | CASTILLO, J.; MACEDO, I.; SILVA, L.; CASTILLO, G.; TERRA, J.A. Productividad y eficiencia de uso del N del cultivo de arroz sobre diferentes antecesores invernales y dosis de N. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B.; Álvarez, O. (Eds.) Arroz 2020. Montevideo (UY): INIA, 2020. p. 64-66. (INIA Serie Técnica; 257)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 74. | | MACEDO, I.; CASTILLO, J.; RICCETTO, S.; BORDAGORRI, A.; TERRA, J.A. Productividad y eficiencia del uso del N en el cultivo de arroz en rotaciones de intensidad variable. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B.; Álvarez, O. (Eds.) Arroz 2020. Montevideo (UY): INIA, 2020. p. 109-112. (INIA Serie Técnica; 257)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 78. | | HERNÁNDEZ, A.; ROVIRA, G.; BORDAGORRI, P.; ESCALANTE, F.; CASTILLO, J.; MARTÍNEZ, S.; MACEDO, I.; TERRA, J.A. Brechas de rendimiento del cultivo de arroz sobre distinto antecesor de verano para dos variedades de alto potencial. In: Zorrilla, G.; Martínez, S.; Terra, J. A. Saravia, H. (Eds.) Arroz 2018. Montevideo (UY): INIA, 2018. p. 81-83. (INIA Serie Técnica; 246)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 79. | | MENDEZ, R.; DEAMBROSI, E.; CASTILLO, J.; MARTINEZ, S.; LAGO, F.; LAUZ, A.; MAGALLANES, A.; SOSA, B.; CASALES, L. Densidad de siembra y dosis de nitrógeno para el cultivar Parao de alto rendimiento. Revista Arroz, 2014, vol. 16, no. 78, p.13-17| Tipo: Artículos en Revistas Agropecuarias |
| Biblioteca(s): INIA Treinta y Tres. |
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| 80. | | MACEDO, I.; CASTILLO, J.; SALDAIN, N.E.; MARTÍNEZ, S.; ZORRILLA DE SAN MARTÍN, G.; TERRA, J.A. Rice yield under contrasting rice rotations systems in a temperate zone of South America. In: International Temperate Rice Conference, 6-9 de marzo, Griffith, NSW, Australia, 2017. 3 p.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| Registros recuperados : 86 | |
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