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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 p| Biblioteca(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. 28| Biblioteca(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. |
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Fecha actual : |
10/09/2014 |
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Actualizado : |
15/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
B - 2 |
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Autor : |
WANG, H.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; FERNANDO, R.L.; VITEZICA, Z.; OKIMOTO, R.; WING, T.; HAWKEN, R.; MUIR, W.M. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Frontiers in Genetics, 2014, v.5, p.1-10. OPEN ACCESS. |
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ISSN : |
1664-8021 |
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DOI : |
10.3389/fgene.2014.00134 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 03 March 2014 // Paper pending published 04 April 2014 // Accepted 25 April 2014 // Published online: 20 May 2014. |
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Contenido : |
ABSTRACT.
The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.
© 2014 Wang, Misztal, Aguilar, Legarra, Fernando, Vitezica, Okimoto, Wing, Hawken and Muir. MenosABSTRACT.
The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation att... Presentar Todo |
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Palabras claves : |
ASSOCIATION MAPPING; BayesB; BODY WEIGHT; BROILER CHICKEN; CARNE DE AVES; GENOME-WIDE ASSOCIATION; SsGWAS. |
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Thesagro : |
ANIMALES DE CARNE; PESO CORPORAL; POLLO. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3067/1/Aguilar-I.-2014-Frontiers-in-Genetics-v.5p.1-10.pdf
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Marc : |
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245 $aGenome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.$h[electronic resource]
260 $c2014
500 $aArticle history: Received 03 March 2014 // Paper pending published 04 April 2014 // Accepted 25 April 2014 // Published online: 20 May 2014.
520 $aABSTRACT. The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined. © 2014 Wang, Misztal, Aguilar, Legarra, Fernando, Vitezica, Okimoto, Wing, Hawken and Muir.
650 $aANIMALES DE CARNE
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653 $aASSOCIATION MAPPING
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700 1 $aMISZTAL, I.
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700 1 $aLEGARRA, A.
700 1 $aFERNANDO, R.L.
700 1 $aVITEZICA, Z.
700 1 $aOKIMOTO, R.
700 1 $aWING, T.
700 1 $aHAWKEN, R.
700 1 $aMUIR, W.M.
773 $tFrontiers in Genetics, 2014$gv.5, p.1-10. OPEN ACCESS.
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