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| Registros recuperados : 75 | |
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| 42. | | LADO, B.; VÁZQUEZ, D.; QUINCKE, M.; SILVA, P.; AGUILAR, I.; GUTIÉRREZ, L. Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. Theoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS. Article history: Received: 29 January 2018 / Accepted: 10 September 2018 / Published online: 19 September 2018.
Supplementary materials.
Acknowledgements: We express our appreciation for the effort of the technical personnel of INIA La...| Biblioteca(s): INIA Las Brujas. |
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| 44. | | ESTEVES, P.; HERNANDEZ, L.; CASTILLO, A.; DALLA RIZZA, M.; QUINCKE, M. Tecnologías para el desarrollo de líneas recombinantes de trigo. MV 20 - COMUNICACIONES LIBRES - MV. MEJORAMIENTO VEGETAL In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 290| Biblioteca(s): INIA Las Brujas. |
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| 47. | | LUIZZI, D.; ABADIE, T.; GATTI, I.; QUINCKE, M.; CONDON, F.; PEREYRA, S.; VÁZQUEZ, D.; DÍAZ DE ACKERMANN, M.; GERMAN, S. Consideraciones finales. Capítulo 7. In: GERMAN, S.; LUIZZI, D. (Ed.). 100 años de mejoramiento de trigo en INIA La Estanzuela. Montevideo (UY): INIA, 2018. p. 68-72.| Biblioteca(s): INIA La Estanzuela. |
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| 48. | | LUIZZI, D.; PEREYRA, S.; QUINCKE, M.; ABADIE, T.; GATTI, I.; DÍAZ DE ACKERMANN, M.; VÁZQUEZ, D.; CONDON, F.; GERMAN, S. Cien años de mejoramiento genético de trigo en La Estanzuela, Uruguay. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. p. 1-2.| Biblioteca(s): INIA La Estanzuela. |
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| 49. | | ESTEVES, P.; MASTROPIERRO, M.; CASTILLO, A.; HERNANDEZ, L.; RODRIGUEZ, M.; DE LEON, W.; PEREIRA, F.; QUINCKE, M. Herramientas biotecnológicas para el mejoramiento genético de cultivos. Revista INIA Uruguay, 2017, no.48, p. 62-66. (Revista INIA; 48)| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
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| 50. | | SILVA, P.; LADO, B.; BRANDARIZ, S.; PEREYRA, S.; GERMAN, S.; VON ZITZEWITZ, J.; GUTIÉRREZ, L.; QUINCKE, M. Herramientas utilizadas y avances en mejoramiento molecular en el Programa de Mejoramiento Genético de Trigo de INIA Uruguay.[Presentación oral]. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. p. 81.| Biblioteca(s): INIA La Estanzuela. |
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| 51. | | SILVA, P.; LADO, B.; BRANDARIZ, S.; BERRO, I.; GUTIÉRREZ, L.; PEREYRA, S.; GERMAN, S.; VON ZITZEWITZ, J.; QUINCKE, M. Herramientas utilizadas y avances en mejoramiento molecular en el programa de mejoramiento genético de trigo de Inia Uruguay. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 277-285. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 52. | | LUIZZI, D.; PEREYRA, S.; ABADIE, T.; GATTI, I.; QUINCKE, M.; VÁZQUEZ, D.; CONDON, F.; DÍAZ DE ACKERMANN, M.; GERMAN, S. Introducción. In: GERMAN, S.; LUIZZI, D. (Ed.). 100 años de mejoramiento de trigo en INIA La Estanzuela. Montevideo (UY): INIA, 2018. p. 5-6.| Biblioteca(s): INIA La Estanzuela. |
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| 53. | | LUIZZI, D.; PEREYRA, S.; ABADIE, T.; GATTI, I.; QUINCKE, M.; CONDON, F.; VÁZQUEZ, D.; DÍAZ DE ACKERMANN, M.; GERMAN, S. Objetivos del mejoramiento genético de trigo. Capítulo 2. In: GERMAN, S.; LUIZZI, D. (Ed.). 100 años de mejoramiento de trigo en INIA La Estanzuela. Montevideo (UY): INIA, 2018. p. 10-16.| Biblioteca(s): INIA La Estanzuela. |
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| 54. | | QUINCKE, M.; PETERSON, C.J.; ZEMETRA, R.S.; HANSEN, J.L.; CHEN, J.; RIERA-LIZARAZU, O.; MUNDT, C.C. Quantitative trait loci analysis for resistance to cephalosporium stripe, a vascular wilt disease of wheat. Theoretical and Applied Genetics, 2011, v.122, No.7, p.1339-1349. Article history: Received: 20 August 2010 / Accepted: 6 January 2011 / Published online: 23 January 2011.| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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| 55. | | GERMAN, S.; PEREYRA, S.; CASTRO, M.; AZZIMONTI, G.; QUINCKE, M.; KOHLI, M.; MADARIAGA, R.; CHAVES , M.; CAMPO, P. Las royas del trigo: situación a nivel regional y amenazas para el cultivo. In: CONGRESO NACIONAL DE TRIGO, 8o. ; SIMPOSIO DE CEREALES DE SIEMBRA OTOÑO INVERNAL, 6o. ; ENCUENTRO DEL MERCOSUR, 2º, Pergamino, Argentina: AIANBA, 14-16 setiembre,2016.| Biblioteca(s): INIA La Estanzuela. |
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| 56. | | AZZIMONTI, G.; DOMENIGUINI, V.; GONZALEZ, N.; GARCIA, R.; SAINT-PIERRE, C.; SINGH, P.; QUINCKE, M.; PEREYRA, S.; GERMAN, S. Three years of the Precision Wheat Phenotyping Platform for diseases in Uruguay: current status and future prospects. [Poster]. In: Proceedings of the Borlaug Global Rust Initiative Technical Workshop ,14-18 April, 2018, Marrakesh, Marruecos.| Biblioteca(s): INIA La Estanzuela. |
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| 57. | | LADO, B.; BATTENFIELD, S.; SILVA, P.; QUINCKE, M.; GUZMAN, C.; SINGH, R.P.; DREISIGACKER, S.; PEÑA, J.; FRITZ, A.; POLAND, J.; GUTIERREZ, L. Comparing strategies to select crosses using genomic prediction in two wheat breeding programs. In: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria. p.88-90.| Biblioteca(s): INIA La Estanzuela. |
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| 58. | | BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. Effect of using imputed missing data on QTL detection on a wheat GWAS panel. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: posters; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. p. 86.| Biblioteca(s): INIA La Estanzuela. |
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| 59. | | BRANDARIZ, S.P.; GONZÁLEZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ , L. Effect of using imputed missing data on QTL detection on a wheat GWAS panel. [Poster]. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 304. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 60. | | SILVA, P.; CALVO-SALAZAR, V.; CONDON, F.; QUINCKE, M.; PRITSCH, C.; GUTIÉRREZ, L.; CASTRO, A.; HERRERA-FOESSEL, S.; VON ZITZEWITZ, J.; GERMAN, S. Effects and interactions of genes Lr34, Lr68 and Sr2 on wheat leaf rust adult plant resistance in Uruguay Euphytica, 2015, v. 204, p. 599?608.| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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| Registros recuperados : 75 | |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
06/12/2019 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
BERRO, I.; LADO, B.; NALIN, R.S.; QUINCKE, M.; GUTIÉRREZ, L. |
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Afiliación : |
Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay.; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA./ Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay. |
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Título : |
Training population optimization for genomic selection. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Plant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028 |
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DOI : |
10.3835/plantgenome2019.04.0028 |
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Idioma : |
Inglés |
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Notas : |
Article histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019. |
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Contenido : |
ABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). MenosABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the
prediction model, the number and type of molecular markers, and the size and composition of the training population (TR).
Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was
to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum
L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization
strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies
to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering
both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic
selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in
populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individ... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; SELECCIÓN GENÓMICA. |
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Thesagro : |
TRIGO; TRITICUM AESTIVUM. |
|
Asunto categoría : |
F01 Cultivo |
|
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16707/1/The-Plant-Genome-2019-Berro-Training-Population-Optimization-for-Genomic-Selection.pdf
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.3835/plantgenome2019.04.0028
|
|
Marc : |
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260 $c2019
500 $aArticle histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019.
520 $aABSTRACT :The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart. Genomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009).
650 $aTRIGO
650 $aTRITICUM AESTIVUM
653 $aGENOMIC SELECTION
653 $aSELECCIÓN GENÓMICA
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773 $tPlant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028
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