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| Registros recuperados : 75 | |
| 27. |  | PEREYRA, S.; CASTRO, M.; GERMÁN, S.; QUINCKE, M.; SILVA, P.; VÁZQUEZ, D.; CAL, A. Avances en el manejo de la fusariosis de la espiga en trigo. Revista INIA Uruguay, 2014, no. 37, p. 43-50 (Revista INIA; 37)| Biblioteca(s): INIA La Estanzuela. |
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| 28. | | CONDON, F.; BERRETTA, A.; QUINCKE, M.; GERMAN, S.; GONZÁLEZ, S.N. Colección de recursos genéticos de trigo en el banco de germoplasma de INIA La Estanzuela. [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. 68. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 29. | | LADO,B.; BATTENFIELD, S.; POLAND, J.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. Comparación de metodologías de predicción de cruzamientos para rendimiento en trigo. MV 14 - 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. 287.| Biblioteca(s): INIA La Estanzuela. |
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| 31. | | LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉREZ, L. Genomic selection in wheat breeding program modelling genotype by environment interaction using mixed models. In: International Wheat Conference, 9., 2015, Sydney, AU. Program and abstracts: posters. Sidney: ICMS Australasia, 2015. p. 168.| Biblioteca(s): INIA La Estanzuela. |
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| 32. | | LADO, B.; MATUS, I.; BELZILE, F.; POLAND, J.; QUINCKE, M.; CASTRO, M.; VON ZITZEWITZ, J. Genotipado por secuenciación en trigo (Triticum aestivum). In: JORNADAS DE LA SOCIEDAD DE BIOQUÍMICA Y BIOLOGÍA MOLECULAR , 8., SIMPOSIO BIOLOGÍA Y BIOTECNOLOGÍA VEGETAL, 11., 2013. Resumen. Montevideo: SBBM, SUB, 2013.| Biblioteca(s): INIA La Estanzuela. |
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| 36. | | VERGES, R.P.; QUINCKE, M.; DÍAZ DE ACKERMANN, M.; GERMAN, S. Mejoramiento genético de trigo: actividades desarrolladas en Dolores en el año 2004. ln: Día de campo de trigo y cebada, 2004, Dolores, Soriano, Uruguay Trabajos presentados. La Estanzuela, Colonia (Uruguay): INIA, 2004. p. 1-4. (Serie Actividades de Difusión; 383).| Biblioteca(s): INIA La Estanzuela. |
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| 37. | | GERMAN, S.; DÍAZ DE ACKERMANN, M.; SILVA, P.; QUINCKE, M.; PEREYRA, S. Mejoramiento por resistencia a enfermedades de trigo en 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. 148-166. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 38. | | GERMAN, S.; PEREYRA, S.; DIAZ DE ACKERMANN, M.; SILVA, P.; QUINCKE, M.; VÁZQUEZ, D. Mejoramiento por resistencia a enfermedades de trigo en 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. 45.| Biblioteca(s): INIA La Estanzuela. |
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| 39. | | ESTEVES, P.; MASTROPIERRO, M.M.; CASTILLO, A.; DALLA RIZZA, M.; BELZILE, F.; HERNANDEZ, L.; QUINCKE, M. Métodos para aumentar la eficiencia del mejoramiento de trigo. 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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| 40. | | CAMPOS, P.; CASTRO, M.; PEREYRA, S.; QUINCKE, M.; MILISICH, H.; GIECO, L.; LÓPEZ, J.; GERMAN, S. Reemergence of stem rust at epidemic levels in Argentina and Uruguay in 2014. In: BGRI Workshop, 2015, Sidney, AU. Poster abstracts. [Ithaca, NY]: Borlaug Global Rust Initiative, 2015.| Biblioteca(s): INIA La Estanzuela. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
03/10/2018 |
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Actualizado : |
24/02/2022 |
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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 : |
LADO, B.; VÁZQUEZ, D.; QUINCKE, M.; SILVA, P.; AGUILAR, I.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO, Universidad de la República (UdelaR)/ Facultad de Agronomía; DANIEL VÁZQUEZ PEYRONEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZ, Universidad de la República (UdelaR)/ Facultad de Agronomía; Universidad de Wisconsin-Madison. |
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Título : |
Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Theoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS. |
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ISSN : |
0040-5752 |
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DOI : |
10.1007/s00122-018-3186-3 |
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Idioma : |
Inglés |
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Notas : |
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 Estanzuela from ?Laboratorio de calidad industrial de granos.? Support for doctoral work of BL was provided by Agencia Nacional de Investigación e Innovación (ANII), Uruguay, through Grant POS_NAC_2013_1_11261 and by Comisión Sectorial de Investigación Científica (CSIC), Uruguay, through grants in the program internships abroad. We would like to thank two anonymous reviewers for their comments that improved the manuscript.
Open Access
Copyright information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
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Contenido : |
KEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.
ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.
© 2018, The Author(s). MenosKEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.
ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped f... Presentar Todo |
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Palabras claves : |
ABILITY TESTING; FORECASTING; GENOMIC PREDICTIONS; PLANT BREEDING PROGRAMS; PLANTS (BOTANY); PLATAFORMA AGROALIMENTOS; QUALITY CONTROL; SOFTWARE TESTING. |
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Thesagro : |
GENES. |
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Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11357/1/Lado2018-Article-ResourceAllocationOptimization.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/12863/1/122-2018-3186-MOESM1-ESM.pdf
https://link.springer.com/content/pdf/10.1007%2Fs00122-018-3186-3.pdf
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Marc : |
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100 1 $aLADO, B.
245 $aResource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article].$h[electronic resource]
260 $c2018
500 $aArticle 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 Estanzuela from ?Laboratorio de calidad industrial de granos.? Support for doctoral work of BL was provided by Agencia Nacional de Investigación e Innovación (ANII), Uruguay, through Grant POS_NAC_2013_1_11261 and by Comisión Sectorial de Investigación Científica (CSIC), Uruguay, through grants in the program internships abroad. We would like to thank two anonymous reviewers for their comments that improved the manuscript. Open Access Copyright information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
520 $aKEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program. © 2018, The Author(s).
650 $aGENES
653 $aABILITY TESTING
653 $aFORECASTING
653 $aGENOMIC PREDICTIONS
653 $aPLANT BREEDING PROGRAMS
653 $aPLANTS (BOTANY)
653 $aPLATAFORMA AGROALIMENTOS
653 $aQUALITY CONTROL
653 $aSOFTWARE TESTING
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700 1 $aAGUILAR, I.
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773 $tTheoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS.
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