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Registros recuperados : 224 | |
181. | | FORNERIS, N. S.; LEGARRA, A.; VITEZICA, Z. G.; TSURUTA, S.; AGUILAR, I.; CANTET, R.J.C.; MISZTAL, I. Quality control of genotypes using heritability estimates of gene content. Volume Genetic Improvement Programs: Selection using molecular information (Posters), 471. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.471.Biblioteca(s): INIA Las Brujas. |
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182. | | LONDOÑO-GIL, M.; LÓPEZ-CORREA, R.; AGUILAR, I.; MAGNABOSCO, C.U.; HIDALGO, J.; BUSSIMAN, F.; BALDI, F.; LOURENCO, D. Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP. Journal of Animal Breeding and Genetics, 2024. https://doi.org/10.1111/jbg.12882 - [Early view] Article history: Received 22 March 2024, Revised 10 May 2024, Accepted 15 May 2024. -- Corresponding author: Londoño-Gil, M.; Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Via de...Biblioteca(s): INIA Las Brujas. |
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183. | | Aguilar, I.; Pravia, M.I.; Ravagnolo, O.; Chiappesoni, G.; Mattos, M.; Ahlig, I.; Urioste, J.; Naya, H. Servicio de evaluación de reproductores Aberdeen Angus Las Brujas, Canelones (Uruguay): INIA, 2004. 23 pBiblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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184. | | LOURENCO, D.; TSURUTA, S.; FRAGOMENI, B.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; MILLER, S.; MOSER, D.; MISZTAL, I. Single-step genomic BLUP for national beef cattle evaluation in US: from initial developments to final implementation. Volume Species - Bovine (beef) 1, 495. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018.Biblioteca(s): INIA Las Brujas. |
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185. | | MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LOURENÇO, D. A. L.; FRAGOMENI, B.; LEGARRA, A.; AGUILAR, I.; LAWLOR, T. J. Single-step genomic evaluations with 570K genotyped animals in US Holsteins. Interbull Bulletin, 2015, v. 49, p. 85-89.Biblioteca(s): INIA Las Brujas. |
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186. | | MASUDA, Y; MISZTAL, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; AGUILAR, I. Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. Journal of Animal Science, 2017, v. 95(1): 49-52. Article history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017.
This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and...Biblioteca(s): INIA Las Brujas. |
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187. | | CARDOSO, F. F.; SOLLERO, B. P.; COMIN, H. B.; GOMES, C. G.; ROSO, V. M.; HIGA, R. H.; CAETANO, A. R.; YOKOO, M. J.; AGUILAR, I. Accuracy of genomic prediction for tick resistance in Braford and Hereford cattle. Volume Species Breeding: Beef cattle (Posters), 713. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.713. Acknowledgments: Research supported by CNPq - National Council for Scientific and Technological Development grant 478992/2012-2, Embrapa - Brazilian Agricultural Research Corporation grants 02.09.07.004 and 01.11.07.002.07, and CAPES -...Biblioteca(s): INIA Las Brujas. |
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188. | | SILVA, D.A.; COSTA, C.N.; SILVA, A.A.; SILVA, H.T.; LOPES, P.S.; SILVA, F.F.; VERONEZE, R.; THOMPSON, G.; AGUILAR, I.; CARVALHEIRA, J. Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle. Journal of Animal Breeding and Genetics, 1 May 2020, Volume 137, Issue 3, Pages 305-315. Doi: https://doi.org/10.1111/jbg.12459 Article history: Received: 10 July 2019 / Revised: 31 October 2019 / Accepted: 3 November 2019 / First published: 08 December 2019.
Funding information: The authors acknowledge the Brazilian Holstein Cattle Breeders Association (ABCBRH)...Biblioteca(s): INIA Las Brujas. |
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189. | | REBOLLO, I.; SCHEFFEL, S.; BLANCO, P.H.; MOLINA, F.; MARTÍNEZ, S.; CARRACELAS, G.; AGUILAR, I.; PÉREZ DE VIDA, F.; ROSAS, J.E. Consolidating twenty-three years of historical data from an irrigated subtropical rice breeding program in Uruguay. Crop Science, 2023. https://doi.org/10.1002/csc2.20955 - [Article in Press]. Article history: First published 15 March 2023. -- Corresponding author: jrosas@inia.org.uy --Biblioteca(s): INIA Las Brujas. |
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190. | | PRAVIA, M.I.; NAVAJAS, E.; DELAFUENTE, J.; LEMA, O.M.; RAVAGNOLO, O.; AGUILAR, I.; CALISTRO, A.; BRITO, G.; PERAZA, P.; CLARIGET, J.M.; DALLA RIZZA, M.; MONTOSSI, F. Construyendo las bases para la selección genómica en la raza Hereford. Eficiencia de conversión y calidad de canal y carne. Revista INIA Uruguay, 2014, no.38, p. 56-59. (Revista INIA; 38)Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó; INIA Treinta y Tres. |
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191. | | NAVAJAS, E.; RAVAGNOLO, O.; AGUILAR, I.; PRAVIA, M.I.; CALISTRO, A.; MACEDO, F.; LEMA, O.M.; DEL PINO, M.L.; DALLA RIZZA, M.; CIAPPESONI, G. EPD Genómicos de eficiencia de conversión en la raza Hereford. Anuario Hereford (Montevideo), p. 176-178, 2017.Biblioteca(s): INIA La Estanzuela. |
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192. | | DE MATTOS, D.; CIAPPESONI, G.; GIMENO, D.; RAVAGNOLO, O.; AGUILAR, I.; DE BARBIERI, I.; MONTOSSI, F.; MARTÍNEZ, H.; FRUGONI, J.C.; GRATTAROLA, M.; PÉREZ JONES, J.; FROS, A. Evaluación genética del núcleo fundacional merino fino: análisis combinado población merino fino - generación 2002. ln: INIA Tacuarembó. Sociedad Criadores Merino Australiano del Uruguay. SUL. Proyecto Merino Fino del Uruguay: cuarta distribución de carneros generados en el Núcleo Fundacional de Merino Fino de la la Unidad Experimental Glencoe, 1999 - 2003. Glencoe, Paysandú, 10 de diciembre, 2003. Tacuarembó (Uruguay): INIA, 2003. p. 59-71 (INIA Serie Actividades de Difusión ; 343)Biblioteca(s): INIA Tacuarembó. |
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197. | | RAVAGNOLO, O.; LEMA, O.M.; CALISTRO, A.; GONZALEZ, I.; LEMES, F.; COSTALES, J.; ZAMIT, W.; AGUILAR, I.; NAVAJAS, E.; SOARES DE LIMA, J.M. Evaluación genética de la raza Hereford 2020. Anuario Hereford (Montevideo), 2020, p. 206-208.Biblioteca(s): INIA La Estanzuela; INIA Tacuarembó. |
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198. | | ROMAN, L.; LA MANNA, A.; ACOSTA, Y.; MENDOZA, A.; AGUILAR, I.; MORALES-PIÑEYRUA, J.; PLA, M.; LAURA ASTIGARRAGA, L.; SARAVIA, C. Evaluación de medidas de mitigación del estrés por calor sobre las respuestas productivas de vacas lecheras de alta producción. In: Día de Campo: producción de forraje y leche en verano. La Estanzuela, Colonia, (Uruguay): INIA, 2013. p. 15. (Serie Actividades de Difusión; 705).Biblioteca(s): INIA La Estanzuela. |
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199. | | NAVAJAS, E.; MACEDO, F.; RAVAGNOLO, O.; AGUILAR, I.; CLARIGET, J.; LEMA, O.M.; PERAZA, P.; PRAVIA, M.I.; DALLA RIZZA, M.; CIAPPESONI, G. Herramientas genómicas para mejorar la eficiencia de alimentación y la calidad de canal de la raza Hereford. 3 - SIMPOSIOS "MEJORA GENÉTICA EN PRODUCCIÓN Y CALIDAD DE CARNE EN ESPECIES DE INTERÉS ECONÓMICO" 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. 28Biblioteca(s): INIA Las Brujas. |
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200. | | LOURENCO, D. A. L.; TSURUTA, S.; FRAGOMENI, B. O.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; BERTRAND, J. K.; AMEN, T. S.; WANG. L.; MOSER, D. W.; MISZTAL, I. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*) Journal of Animal Science, 2015, v. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS. (*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s...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 : |
15/10/2014 |
Actualizado : |
15/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
A - 1 |
Autor : |
CHEN, C.Y.; MISZTAL, I.; AGUILAR, I.; TSURUTA, S.; MEUWISSEN, T.H.E.; AGGREY, S.E.; WING, T.; MUIR, W.M. |
Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
Título : |
Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens. |
Fecha de publicación : |
2011 |
Fuente / Imprenta : |
Journal of Animal Science, 2011, v.89, no.1, p.23-28. |
ISSN : |
0021-8812 |
DOI : |
10.2527/jas.2010-3071 |
Idioma : |
Inglés |
Notas : |
Article history: Received April 9, 2010 / Accepted September 22, 2010. |
Contenido : |
ABSTRACT.
Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/ FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based.
©2011 American Society of Animal Science. MenosABSTRACT.
Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and... Presentar Todo |
Thesagro : |
MARCADORES GENÉTICOS; POLLO. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
Marc : |
LEADER 02840naa a2200265 a 4500 001 1051115 005 2019-10-15 008 2011 bl uuuu u00u1 u #d 022 $a0021-8812 024 7 $a10.2527/jas.2010-3071$2DOI 100 1 $aCHEN, C.Y. 245 $aGenome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step$bAn example using broiler chickens.$h[electronic resource] 260 $c2011 500 $aArticle history: Received April 9, 2010 / Accepted September 22, 2010. 520 $aABSTRACT. Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/ FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based. ©2011 American Society of Animal Science. 650 $aMARCADORES GENÉTICOS 650 $aPOLLO 700 1 $aMISZTAL, I. 700 1 $aAGUILAR, I. 700 1 $aTSURUTA, S. 700 1 $aMEUWISSEN, T.H.E. 700 1 $aAGGREY, S.E. 700 1 $aWING, T. 700 1 $aMUIR, W.M. 773 $tJournal of Animal Science, 2011$gv.89, no.1, p.23-28.
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