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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
03/10/2018 |
Actualizado : |
24/02/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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
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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.
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INIA Las Brujas (LB) |
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141. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LOURENCO, D.; LEGARRA, A.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; MISZTAL, I. Single-step genomic evaluations from theory to practice: using snp chips and sequence data in blupf90. Genes, July 2020. Volume 11, Issue 7, Article number 790, Pages 1-32. Open Access. Doi: https://doi.org/10.3390/genes11070790 Article history: Received: 19 June 2020 / Revised: 3 July 2020 / Accepted: 6 July 2020 / Published: 14 July 2020.
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data):...Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Las Brujas. |
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142. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | CIAPPESONI, G.; NAVAJAS, E.; BAPTISTA, R.; AGUILAR, I.; PERAZA, P.; CARRACELAS, B.; DE BARBIERI, I. Proyecto SMARTER. INIA ya está en el Mundial de la Genética Ovina. Producción Animal. Revista INIA Uruguay, Marzo 2022, no.68, p.15-18. (Revista INIA; 68).Tipo: Artículos en Revistas Agropecuarias |
Biblioteca(s): INIA Las Brujas. |
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143. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LOURENCO, D.; TSURUTA, S.; AGUILAR, I.; MASUDA, Y.; BERMANN, M.; LEGARRA, A.; MISZTAL, I. Recent updates in the BLUPF90 software suite. [366]. Part 19 - Methods and tools: software and computing strategies. 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_366 1530-1533. Article history: Published online: February 9, 2023. -- Corresponding author: D. Lourenco, email: danilino@uga.eduTipo: Trabajos en Congresos/Conferencias |
Biblioteca(s): INIA Las Brujas. |
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145. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LOURENCO, D.A.L.; MISZTAL, I.; WANG, H.; AGUILAR, I.; TSURUTA, S.; BERTRAND, J.K. Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models. Journal of Animal Science, 2013, v.91, no.9, p.4090-4098. Article history: Published online July 26, 2013.
This study was partially funded by the American Angus Association (St. Joseph, MO) and the USDA Agriculture and Food Research Initiative (Grant no. 2009-65205-05665 from the USDA National...Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 2 |
Biblioteca(s): INIA Las Brujas. |
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146. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | NAVAJAS, E.; RAVAGNOLO, O.; AGUILAR, I.; CIAPPESONI, G.; PERAZA, P.; DALLA RIZZA, M.; MONTOSSI, F. Selección genómica animal: quién, cómo y dónde. ln: INIA TACUAREMBÓ. UNIDAD DE BIOTECNOLOGÍA INIA. Jornada técnica. Jornada de Agrobiotecnología INIA, 15 NOVIEMBRE, Tacuarembó, Biotecnología para el sector productivo: situación actual y perspectivas. Tacuarembó (Uruguay): INIA, 2012. p. 17-19 (INIA Serie Actividades de Difusión; 702) INIA TacuarembóBiblioteca(s): INIA Tacuarembó. |
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147. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | MISZTAL, I.; AGUILAR, I.; TSURUTA, S.; SÁNCHEZ, J.P.; ZUMBACH, B. Studies on heat stress in dairy cattle and pigs. Volume Special topics: Animal breeding and the environmental challenges - Lecture Sessions, 0625. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0625.Tipo: Trabajos en Congresos/Conferencias |
Biblioteca(s): INIA Las Brujas. |
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148. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP. Genetics, Selection, Evolution : GSE, 2022, Volume 54, Issue 1, Pages 66. OPEN ACCESS. doi: https://doi.org/10.1186/s12711-022-00752-4 Article history: Received 22 March 2022; Accepted 23 August 2022; Published 27 September 2022.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - 1 |
Biblioteca(s): INIA Las Brujas. |
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149. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; JOHNSON, D.; TSURUTA, S.; LAWLOR, T. J. A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation. Volume Methods and tools: Software and bioinformatics - Lecture Sessions, 0050. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0050. Acknowledgements: This study was partially funded by the Holstein Association, Smithfield Premium Genetics, and AFRI grants 2009-65205-05665 and 2010-65205-20366 from the USDA NIFA Animal Genome Program.Tipo: Trabajos en Congresos/Conferencias |
Biblioteca(s): INIA Las Brujas. |
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151. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | FRAGOMENI, B.O.; LOURENCO, D.A.L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; MISZTAL, I. Use of genomic recursions and algorithm for proven and young animals for single-step genomic BLUP analyses - a simulation study. Journal of Animal Breeding and Genetics, 2015, v.132, no.5, p. 340-345.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Las Brujas. |
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152. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | AGUILAR, I.; MISZTAL, I.; JOHNSON, D. L.; LEGARRA, A.; TSURUTA, S.; LAWLOR, T. J. Uso de información genómica en evaluaciones genéticas. Agrociencia Uruguay, 2010, v. 14, no. 3, p. 43-47. Agrociencia, Nro especial: Congreso Asociación Uruguaya de Producción Animal, 3., 4-5 Noviembre 2010, Montevideo, UY: INIA, Facultad de Agronomía, SMVU.Tipo: Artículos en Revistas Indexadas Nacionales | Circulación / Nivel : B - 5 |
Biblioteca(s): INIA Las Brujas. |
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153. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | MOTTA, R. R.; SILVA, F. F.; LOPES, P. S.; TEMPELMAN, R. J.; SOLLERO, B. P.; AGUILAR, I.; CARDOSO, F. F. Analyses of reaction norms reveal new chromosome regions associated with tick resistance in cattle. Animal, 2018, volume 12, Issue 2, pages 205-214. OPEN ACCESS. doi: https://doi.org/10.1017/S1751731117001562 Article history: Received 12 December 2016; Accepted 22 May 2017; Published online: 13 July 2017.
Corresponding author: R.R. Mota, Gembloux Agro-Bio Tech Faculty, TERRA Teaching and Research Centre, University of Liège, B-5030 Gembloux,...Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Las Brujas. |
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154. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LEMA, O.M.; BRITO, G.; CLARIGET, J.; PEREZ, E.; LA MANNA, A.; RAVAGNOLO, O.; AGUILAR, I.; MONTOSSI, F. Dos años de evaluación de ganancia diaria invernal de terneros con paternidad conocida sobre la recría y terminación.[Presentación oral]. In: CONGRESO ARGENTINO DE PRODUCCIÓN ANIMAL, 38., 2015. Resúmenes. Santa Rosa, La Pampa, AR: ASAS/AAPA, 2015Tipo: Presentaciones Orales |
Biblioteca(s): INIA Treinta y Tres. |
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155. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; LAWLOR, T.J.; FORNI, S.; WELLER, J.I. Are evaluations on young genotyped animals benefiting from the past generations?. Journal of Dairy Science, 2014, v.97, no.6, p.3930-3942. OPEN ACCESS Article history: Received November 26, 2013. // Accepted February 11, 2014. OPEN ACCESSTipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 1 |
Biblioteca(s): INIA Las Brujas. |
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156. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LOURENCO, D.A.L.; FRAGOMENI, B.O.; TSURUTA, S.; AGUILAR, I.; ZUMBACH, B.; HAWKEN, R.J.; LEGARRA, A.; MISZTAL, I. Accuracy of estimated breeding values with genomic information on males, females, or both: An example on broiler chicken. Genetics Selection Evolution, 2015, v. 242, p. 47-56. OPEN ACCESS. Article history: Received: 14 October 2014 / Accepted: 22 June 2015 / Published: 02 July 2015.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Las Brujas. |
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157. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | RAVAGNOLO, O.; AGUILAR, I.; CROWLEY, J. J.; PRAVIA, M.I.; LEMA, O.M.; MACEDO, F.; SCOTT, S.; NAVAJAS, E. Accuracy of genomic predictions of residual feed intake in Hereford with Uruguayan and Canadian training populations. Volume: Electronic Poster Session - Species - Bovine (beef) 1, p. 723. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018. 6 p.Tipo: Trabajos en Congresos/Conferencias |
Biblioteca(s): INIA Las Brujas. |
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158. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | LEMA, O.M.; BRITO, G.; CLARIGET, J.; PEREZ, E.; LA MANNA, A.; RAVAGNOLO, O.; AGUILAR, I.; MONTOSSI, F. Can nutritional level and parental EPD for rib eye area influence feed conversion efficiency and carcass yield in steers?.[Poster]. In: AUSTRALIAN SOCIETY OF ANIMAL PRODUCTION; NEW ZEALAND SOCIETY OF ANIMAL PRODUCTION, 31st,2016. Proceedings. Adelaida, South Australia, AU: ASAP. 2016.Biblioteca(s): INIA La Estanzuela. |
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159. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | NAVAJAS, E.; PERAZA, P.; RAVAGNOLO, O.; CIAPPESONI, G.; AGUILAR, I.; KELLY, L.; BRANDA, A.; DALLA RIZZA, M.; MONTOSSI, F. Banco de ADN genómico animal: pilar de una plataforma en selección genómica. Revista INIA Uruguay, 2012, no. 28, p. 20-24 (Revista INIA; 28)Tipo: Artículos en Revistas Agropecuarias |
Biblioteca(s): INIA Las Brujas. |
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160. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | CARRACELAS, B.; PERAZA, P.; VERGARA, A.; CIAPPESONI, G.; RAVAGNOLO, O.; AGUILAR, I.; LEMA, O.M.; NAVAJAS, E. Banco de ADN genómico animal - plataforma de evaluación genómica. Revista INIA Uruguay, Diciembre 2022, no.71, p. 38-42. (Revista INIA; 71)Tipo: Artículos en Revistas Agropecuarias |
Biblioteca(s): INIA Treinta y Tres. |
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