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Registros recuperados : 224 | |
161. | | 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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162. | | 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.Biblioteca(s): INIA Las Brujas. |
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163. | | 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.Biblioteca(s): INIA Las Brujas. |
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164. | | ROSAS, J.E.; ALE, L.; REBOLLO, I.; SCHEFFEL, S.; AGUILAR, I.; MOLINA, F.; PÉREZ DE VIDA, F. Boosting INIA's Rice Breeding Program with molecular quantitative genetics approaches. [Abstract]. In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.Biblioteca(s): INIA Treinta y Tres. |
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165. | | BALMELLI, G.; SIMETO, S.; CASTILLO, A.; GASPARRI, P.; CABRERA, D.; AGUILAR, I.; QUEZADA, M.; DA SILVA, C.; GIORELLO, F. Breeding for resistance to Teratosphaeria nubilosa on Eucalyptus globulus. In: Pesquisa florestal brasileira = Brazilian journal of forestry research., v. 39, e201902043, Special issue, 2019. Colombo : Embrapa Florestas, 2019. Congreso IUFRO, 25., Curitiba, Brasil, 29 setiembre-05 octubre, 2019. Abstracts. p. 173Biblioteca(s): INIA Tacuarembó. |
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166. | | 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)Biblioteca(s): INIA Las Brujas. |
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167. | | 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)Biblioteca(s): INIA Treinta y Tres. |
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168. | | MISZTAL, I.; FRAGOMENI, B.; LOURENÇO, D. A. L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T. J. Efficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY). Interbull Bulletin, 2015, v. 49, p. 111-116.Biblioteca(s): INIA Las Brujas. |
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169. | | FRAGOMENI, B.O.; LOURENCO, D.A.L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T.J.; MIZTAL, I. Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes. Journal of Dairy Science, 2015, v.98, no.6, p.4090-4094. OPEN ACCESS. Article history: Received November 18, 2014 / Accepted March 13, 2015 / Published online: April 8, 2015.Biblioteca(s): INIA Las Brujas. |
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170. | | CHEN, C. Y.; MISZTAL, I.; AGUILAR, I.; TSURUTA, S.; MEUWISSEN, T.; AGGREY, S. E.; MUIR, W. M. Genome wide marker assisted selection in chicken: making the most of all data, pedigree, phenotypic, and genomic in a simple one step procedure. Volume Genetic improvement programmes: Selection using molecular information - Lecture Sessions, 0288. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0288. Acknowledgements: The authors thank Cobb-Vantress for access to data for this study. This study was partially funded by the Holstein Association, Smithfield Premium Genetics, and by AFRI grants 2009-65205-05665 and 2010-65205-20366 from...Biblioteca(s): INIA Las Brujas. |
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171. | | BRUNES, L.C.; FARIA, C.U.D.; MAGNABOSCO, C.U.; LOBO, R.B.; PERIPOLLI, E.; AGUILAR, I.; BALDI, F. Genomic prediction ability and genetic parameters for residual feed intake calculated using different approaches and their associations with growth, reproductive, and carcass traits in Nellore cattle. Journal of Applied Genetics, 2023, Volume 64, Issue 1, Pages 159 - 167. doi: https://doi.org/10.1007/s13353-022-00734-8 Article history: Received 25 February 2022; Revised 3 September 2022; Accepted 26 October 2022; Published online 15 November 2022; Published February 2023. -- Corresponding author: Brunes, L.C.; Animal Performance Center, Embrapa...Biblioteca(s): INIA Las Brujas. |
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172. | | GRASSO, A.; GOLDBERG, V.; NAVAJAS, E.; IRIARTE, W.; GIMENO, D.; AGUILAR, I.; MEDRANO, J.F.; RINCÓN, G.; CIAPPESONI, G. Genomic variation and population structure detected by single nucleotide polymorphism arrays in Corriedale, Merino and Creole sheep. Genetics and Molecular Biology, 2014, v.37, n.2, p.389-395. Article history: Received: August 29, 2013 / Accepted: March 16, 2014.Biblioteca(s): INIA Las Brujas. |
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174. | | RODRÍGUEZ, J.D.; PERIPOLLI, E.; LONDOÑO-GIL, M.; ESPIGOLAN, R.; LÔBO, R. B.; LÓPEZ-CORREA, R.; AGUILAR, I.; BALDI, F. Effect of minor allele frequency and density of single nucleotide polymorphism marker arrays on imputation performance and prediction ability using the single-step genomic Best Linear Unbiased Prediction in a simulated beef cattle population. Research paper. Animal Production Science. 2023, volume 63, issue 9, p. 844-852. https://doi.org/10.1071/AN21581 Article history: Submitted 1 December 2021, Accepted 1 March 2023, Published 4 April 2023. -- Correspondence to: Juan Diego Rodríguez,
Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrarias e Veterinárias, Departamento...Biblioteca(s): INIA Las Brujas. |
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175. | | NEGRI, R.; AGUILAR, I.; FELTES, G. L.; MACHADO, J. D.; NETO, J. B.; COSTA-MAIA, F. M.; COBUCI, J. A. Inclusion of bioclimatic variables in genetic evaluations of dairy cattle.[Open Access]. Animal Bioscience [Asian-Australasian Journal of Animal Sciences], Volume 34, Issue 2, February 2021, Pages 163-171. Doi: https://doi.org/10.5713/ajas.19.0960 Article history: Submitted Dec 16, 2019 / Revised Mar 27, 2020 / Accepted Apr 28, 2020.
Corresponding Author: rn.negri@yahoo.comBiblioteca(s): INIA Treinta y Tres. |
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176. | | MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; AGUILAR, I.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; LAWLOR, T.J. Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. Journal of Dairy Science, 2016, v.99, no.3, p.1968-1974. OPEN ACCESS OPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016Biblioteca(s): INIA Las Brujas. |
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177. | | MCWHORTER, T.M.; BERMANN, M.; GARCIA, A.L.S.; LEGARRA, A.; AGUILAR, I.; MISZTAL, I.; LOURENCO, D. Implication of the order of blending and tuning when computing the genomic relationship matrix in single-step GBLUP. Journal of Animal Breeding and Genetics, 2023, volume 140, issue 1, pp. 60-78. OPEN ACCESS. doi: https://doi.org/10.1111/jbg.12734 Article history: Received 18 March 2019; Revised 15 July 2019; Accepted: 29 July 2019; First published 10 August 2022.
Correspondence: McWhorter, T.M.; Department of Animal and Dairy Science, University of Georgia, Athens, GA, United...Biblioteca(s): INIA Las Brujas. |
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178. | | RAVAGNOLO, O.; NAVAJAS, E.; AGUILAR, I.; CIAPPESONI, G.; LEMA, O.M.; MONTOSSI, F.; PERAZA, P.; DALLA RIZZA, M. Mejoramiento genético animal e importancia del banco de ADN. 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. 4-7 (INIA Serie Actividades de Difusión; 702)Biblioteca(s): INIA Tacuarembó. |
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179. | | LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; EZRA, E.; RON, M.; SHIRAK, A.; WELLER, J.I. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science, 2014, v.97, no.3, p.1742-1752. OPEN ACCESS. Article history: Received September 10, 2013. / Accepted December 6, 2013.Biblioteca(s): INIA Las Brujas. |
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180. | | FORNERIS, N. S.; LEGARRA, A.; VITEZICA, Z. G.; TSURUTA, S.; AGUILAR, I.; MISZTAL, I.; CANTET, R. J. C. Quality control of genotypes using heritability estimates of gene content at the marker. Genetics, 2015, v. 199, p. 675-681. OPEN ACCESS. Manuscript received September 26, 2014; accepted for publication December 18, 2014; published Early Online January 6, 2015.Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 224 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
23/05/2016 |
Actualizado : |
11/12/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; AGUILAR, I.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; LAWLOR, T.J. |
Afiliación : |
Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; INRA (Institut National de la Recherche Agronomique); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Animal and Dairy Science, University of Georgia; Department of Animal and Dairy Science, University of Georgia; Holstein Association USA Inc. |
Título : |
Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
Journal of Dairy Science, 2016, v.99, no.3, p.1968-1974. OPEN ACCESS |
DOI : |
10.3168/jds.2015-10540 |
Idioma : |
Inglés |
Notas : |
OPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016 |
Contenido : |
ABSTRACT.
The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up View the MathML source for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals.
© 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). MenosABSTRACT.
The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 rand... Presentar Todo |
Palabras claves : |
FINAL SCORE; GENOMIC EVALUATION; GENOMIC RELATIONSHIP MATRIX. |
Thesagro : |
SsGBLUP; TORO. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12160/1/1-s2.0-S0022030216000825-main.pdf
|
Marc : |
LEADER 03610naa a2200289 a 4500 001 1054839 005 2018-12-11 008 2016 bl uuuu u00u1 u #d 024 7 $a10.3168/jds.2015-10540$2DOI 100 1 $aMASUDA, Y. 245 $aImplementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.$h[electronic resource] 260 $c2016 500 $aOPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016 520 $aABSTRACT. The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix View the MathML source based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up View the MathML source for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals. © 2016, THE AUTHORS. Published by FASS and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). 650 $aSsGBLUP 650 $aTORO 653 $aFINAL SCORE 653 $aGENOMIC EVALUATION 653 $aGENOMIC RELATIONSHIP MATRIX 700 1 $aMISZTAL, I. 700 1 $aTSURUTA, S. 700 1 $aLEGARRA, A. 700 1 $aAGUILAR, I. 700 1 $aLOURENCO, D.A.L. 700 1 $aFRAGOMENI, B.O. 700 1 $aLAWLOR, T.J. 773 $tJournal of Dairy Science, 2016$gv.99, no.3, p.1968-1974. OPEN ACCESS
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