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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 : |
21/02/2014 |
Actualizado : |
18/12/2018 |
Tipo de producción científica : |
Artículos Indexados |
Autor : |
MISZTAL, I.; FRAGOMENI, B.; LOURENÇO, D. A. L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T. J. |
Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Efficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY). |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Interbull Bulletin, 2015, v. 49, p. 111-116. |
Idioma : |
Inglés |
Contenido : |
ABSTRACT.
The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and
GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular inverse. The inverse with APY is computed from G, which in turn is derived from single nucleotide polymorphism (SNP) BLUP and indirectly from BayesB or other SNP-based prediction methods. Strategies like SNP selection, SNP weighting, and use of causative SNPs from sequence analysis can be incorporated in APY without additional cost. The APY removes size limitations from ssGBLUP and facilitates a model with a complex genetic architecture. MenosABSTRACT.
The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and
GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular... Presentar Todo |
Palabras claves : |
BIG POPULATION; GENOMIC RECURSION; INVERSION; SINGLE-STEP METHOD. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12204/1/1387-2387-1-PB.pdf
|
Marc : |
LEADER 02658naa a2200253 a 4500 001 1012458 005 2018-12-18 008 2015 bl uuuu u00u1 u #d 100 1 $aMISZTAL, I. 245 $aEfficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY).$h[electronic resource] 260 $c2015 520 $aABSTRACT. The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular inverse. The inverse with APY is computed from G, which in turn is derived from single nucleotide polymorphism (SNP) BLUP and indirectly from BayesB or other SNP-based prediction methods. Strategies like SNP selection, SNP weighting, and use of causative SNPs from sequence analysis can be incorporated in APY without additional cost. The APY removes size limitations from ssGBLUP and facilitates a model with a complex genetic architecture. 653 $aBIG POPULATION 653 $aGENOMIC RECURSION 653 $aINVERSION 653 $aSINGLE-STEP METHOD 700 1 $aFRAGOMENI, B. 700 1 $aLOURENÇO, D. A. L. 700 1 $aTSURUTA, S. 700 1 $aMASUDA, Y. 700 1 $aAGUILAR, I. 700 1 $aLEGARRA, A. 700 1 $aLAWLOR, T. J. 773 $tInterbull Bulletin, 2015$gv. 49, p. 111-116.
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