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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
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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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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001 1059141
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008 2018 bl uuuu u00u1 u #d
022 $a0040-5752
024 7 $a10.1007/s00122-018-3186-3$2DOI
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
700 1 $aVÁZQUEZ, D.
700 1 $aQUINCKE, M.
700 1 $aSILVA, P.
700 1 $aAGUILAR, I.
700 1 $aGUTIÉRREZ, L.
773 $tTheoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS.
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INIA Las Brujas (LB) |
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| 4. |  | HAVIS, N.D.; BROWN, J.K.M.; CLEMENTE, G.; FREI, P.; JEDRYCZKA, M.; KACZMAREK, J.; KACZMAREK, M.; MATUSINSKY, P.; MCGRANN, G.R.D.; PEREYRA, S.; PIOTROWSKA, M.; SGHYER, H.; TELLIER, A.; HESS, M. Ramularia collo-cygni: an emerging pathogen of barley crops. Phytophathology, v. 105, n. 7, p. 895-904, 2015. OPEN ACCESS. Article History: Issue Date: 23 Jul 2015 / Published: 29 Jun 2015 / First Look: 27 Jan 2015 / Accepted: 16 Jan 2015.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 1 |
| Biblioteca(s): INIA La Estanzuela. |
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