| |
|
|
| Registros recuperados : 224 | |
| 121. |  | CIAPPESONI, G.; GIMENO, D.; RAVAGNOLO, O.; DE BARBIERI, I.; AGUILAR, I.; MONTOSSI, F.; GRATTAROLA, M. Evaluación genética preliminar del Núcleo Fundacional Merino Fino: análisis combinado población Merino fino - generación 2003. ln: INIA Tacuarembó. Sociedad Criadores Merino Australiano del Uruguay. SUL. Proyecto Merino Fino del Uruguay: quinta distribución de carneros generados en el núcleo fundacional de merino fino de la Unidad Experimental Glencoe, INIA Tacuarembó, 1999 - 2004. Glencoe, Paysandú, 10 diciembre, 2004. Tacuarembó (Uruguay): INIA, 2004. p. 69-83 (INIA Serie Actividades de Difusión ; 392)| Biblioteca(s): INIA Tacuarembó. |
|    |
| 122. | | LEMA, O.M.; AGUILAR, I.; DIONELLO, N.J.L.; GIMENO, D.; CARDOSO, F.F. Growth performance for crossbreed Hereford, Angus, Salers and Nellore. In: CONGRESO ARGENTINO DE PRODUCCIÓN ANIMAL, 34.; JOINT MEETING AAPA-ASAS, 1., 2011, Mar del Plata. Resúmenes. Mar del Plata: ASAS/AAPA, 2011. Revista Argentina de Producción Animal, v. 31, suppl.1. p. 133, 2011.| Biblioteca(s): INIA La Estanzuela. |
|  |
| 123. | | AGUILAR, I.; LEGARRA, A.; CARDOSO, F.; MASUDA, Y.; LOURENCO, D.; MISZTAL, I. Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. (Short Communication) Genetics Selection Evolution, 20 June 2019, v. 51, Issue 1, Article number 28. OPEN ACCESS. Article history: Received: 3 January 2019 // Accepted: 27 May 2019 // Published Online: 20 June 2019.
Funding text: This study was partially funded by the American Angus Association (St. Joseph, MO) and by Agriculture and Food Research...| Biblioteca(s): INIA Las Brujas. |
| |
| 124. | | AGUILAR, I.; MISZTAL, I.; JOHNSON, D.L.; LEGARRA, A.; TSURUTA, S.; LAWLOR, T.J. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 2010, v. 93, no. 2, p. 743-752. OPEN ACCESS Article history: Received September 14, 2009 / Accepted November 10, 2009 / Published in issue: February 2010.| Biblioteca(s): INIA Las Brujas. |
|  |
| 125. | | MISZTAL, I.; LOURENCO, D.; TSURUTA, S.; AGUILAR, I.; MASUDA, Y.; BERMANN, M.; CESARANI, A.; LEGARRA, A. How ssGBLUP became suitable for national dairy cattle evaluations. [668]. Part 37 - Bovine dairy - genetic evaluation methods. In: Proceedings of the World Congress on Genetics Applied to Livestock Production (WCGALP), 12., Rotterdam, the Netherlands, 3-8 July 2022. doi: https://doi.org/10.3920/978-90-8686-940-4_668 2757-2760. Article history: Published online: February 9, 2023 -- Corresponding author: I. Misztal, email: ignacy@uga.edu| Biblioteca(s): INIA Las Brujas. |
| |
| 127. | | SIGDEL, A.; LIU, L.; ABDOLLAHI-ARPANAHI, R.; AGUILAR, I.; PEÑAGARICANO, F. Genetic dissection of reproductive performance of dairy cows under heat stress. Animal Genetics, 1 August 2020, Volume 51, Issue 4, Pages 511-520. OPEN ACCESS. Doi: https://doi.org/10.1111/age.12943 Article history: Accepted for publication 31 March 2020. / First published:03 May 2020.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any...| Biblioteca(s): INIA Las Brujas. |
| |
| 128. | | RAVAGNOLO, O.; BRITO, G.; AGUILAR, I.; CIAPPESONI, G.; LEMA, O.M. Genetic parameters for ultrasound live traits in pasture fed Angus cattle. Volume Session:Species breeding: Beef cattle breeding - Poster Sessions, 0617. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0617. Acknowledgements: The authors thank the Uruguayan Aberdeen Angus Breed Association, ARU and the University of Agronomy for their collaboration during the last years. This work could not have been possible without financing and technical...| Biblioteca(s): INIA Las Brujas. |
| |
| 130. | | ANTONIOS, S.; RODRÍGUEZ-RAMILO, S.T.; AGUILAR, I.; ASTRUC, J.M.; LEGARRA, A.; VITEZICA, Z. G. Genomic and pedigree estimation of inbreeding depression for semen traits in the Basco-Béarnaise dairy sheep breed. Journal of Dairy Science, 2021, volume 104, number 3, pages 3221-3230. doi: https://doi.org/10.3168/jds.2020-18761 Article history: Receibed: April 21, 2020 / Accepted: October 5, 2020 /First Publication: 23 Decembrer 2020.
Corresponding author: zulma.viterica@inrae.fr| Biblioteca(s): INIA Treinta y Tres. |
| |
| 131. | | MOTA, R.R.; TEMPELMAN, R.J.; FERNANDO F CARDOSO; AGUILAR, I.; LOPES, P.S. Genomic wide-selection for tick resistance in Hereford and Braford cattle via reaction norm models. Volume Species Breeding: Beef cattle, 235. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.235. Acknowledgments: The authors thank Delta G Connection by providing the data used in this research; Embrapa Southern Region Animal Husbandry and Michigan State University for theoretical and technical support; CAPES, CNPq and FAPEMIG by...| Biblioteca(s): INIA Las Brujas. |
| |
| 132. | | REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. Original article. Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy...| Biblioteca(s): INIA Las Brujas. |
| |
| 133. | | CHEN, C.Y.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; MUIR, W.M. Effect of different genomic relationship matrices on accuracy and scale. Journal of Animal Science, 2011, v.89, no.9, p.2673-2679. Article history: Received September 29, 2010. / Accepted March 21, 2011.| Biblioteca(s): INIA Las Brujas. |
| |
| 134. | | AGUILAR, I.; FERNÁNDEZ, E.N.; BLASCO, A.; RAVAGNOLO, O.; LEGARRA, A. Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP. Journal of Animal Breeding and Genetics, 1 July 2020, Volume 137, Issue 4, pp. 356-364. Doi: https://doi.org/10.1111/jbg.12470 Article history: Received: 22 August 2019 / Revised: 10 December 2019 / Accepted: 11 January 2020 / First published:20 February 2020
Corresponding author: Aguilar, I.; email:iaguilar@inia.org.uy
Funding information: FEDER; INRA;...| Biblioteca(s): INIA Las Brujas. |
| |
| 137. | | TSURUTA, S.; AGUILAR, I.; MISZTAL, I.; LEGARRA, A.; LAWLOR, T. J. Multiple trait genetic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Volume Genetic improvement programmes: Selection using molecular information - Poster Sessions, 0489. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0489.| Biblioteca(s): INIA Las Brujas. |
| |
| 139. | | MACEDO, F.; NAVAJAS, E.; AGUILAR, I.; GRASSO, A.; PIERUCCIONI, F.; CIAPPESONI, G. New parentage testing SNP panel for commercial breeds will be a useful tool for conservation of creole sheep. Volume Genetic Improvement Programs: Selection for harsh environments and management of animal genetic resources (Posters), , 441. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p. 441. 3 p.| Biblioteca(s): INIA Las Brujas. |
| |
| Registros recuperados : 224 | |
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha actual : |
03/10/2018 |
|
Actualizado : |
24/02/2022 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Circulación / Nivel : |
Internacional - -- |
|
Autor : |
LADO, B.; VÁZQUEZ, D.; QUINCKE, M.; SILVA, P.; AGUILAR, I.; GUTIÉRREZ, L. |
|
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. |
|
Título : |
Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
|
Fecha de publicación : |
2018 |
|
Fuente / Imprenta : |
Theoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS. |
|
ISSN : |
0040-5752 |
|
DOI : |
10.1007/s00122-018-3186-3 |
|
Idioma : |
Inglés |
|
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. |
|
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 |
|
Palabras claves : |
ABILITY TESTING; FORECASTING; GENOMIC PREDICTIONS; PLANT BREEDING PROGRAMS; PLANTS (BOTANY); PLATAFORMA AGROALIMENTOS; QUALITY CONTROL; SOFTWARE TESTING. |
|
Thesagro : |
GENES. |
|
Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
|
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
|
|
Marc : |
LEADER 03937naa a2200325 a 4500
001 1059141
005 2022-02-24
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.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
| Expresión de búsqueda válido. Check! |
|
|
