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| Registros recuperados : 13 | |
| 1. |  | FRAGOMENI, B.O.; MISZTAL, I.; LOURENCO, D.L.; AGUILAR, I.; OKIMOTO, R.; MUIR, W.M. Changes in variance explained by top SNP windows over generations for three traits in broiler chicken Frontiers in Genetics, 2014, v.5, no.Oct., Article number 332. OPEN ACCESS. Article history: Published 01 October 2014.| Biblioteca(s): INIA Las Brujas. |
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| 2. | | LOURENÇO, D. A. L.; MISZTAL, I.; TSURUTA, S.; FRAGOMENI, B.; AGUILAR, I.; MASUDA, Y.; MOSER, D. Direct and indirect genomic evaluations in beef cattle. Interbull Bulletin, 2015, v. 49, p.80 - 84.| Biblioteca(s): INIA Las Brujas. |
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| 3. | | 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.| Biblioteca(s): INIA Las Brujas. |
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| 4. | | 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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| 5. | | 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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| 6. | | 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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| 7. | | 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 2016| Biblioteca(s): INIA Las Brujas. |
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| 8. | | 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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| 9. | | 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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| 10. | | 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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| 11. | | 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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| 12. | | LOURENCO, D.A.L.; FRAGOMENI, B.O.; BRADFORD, H.L.; MENEZES I.R.; FERRAZ, J.B.S.; AGUILAR, I.; MISZTAL, I. Implications of SNP weighting on single-step genomic predictions for different reference population sizes. Journal of Animal Breeding and Genetics, 2017, v. 134 (6), p. 463-471. Article history: Received: 28 February 2017 / Accepted: 19 July 2017.
This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI) and by Agriculture and Food Research Initiative Competitive...| Biblioteca(s): INIA Las Brujas. |
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| 13. | | MISZTAL, I.; WANG, H.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.; FRAGOMENI, B. O.; ZHANG, X.; MUIR, W. M.; CHENG, H. H.; OKIMOTO, R.; WING, T.; HAWKEN, R. R.; ZUMBACH, B.; FERNANDO, R. GWAS using ssGBLUP. Volume Species Breeding: Poultry, 325. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.325.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 13 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/05/2016 |
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Actualizado : |
11/12/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; AGUILAR, I.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; LAWLOR, T.J. |
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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. |
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Título : |
Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2016, v.99, no.3, p.1968-1974. OPEN ACCESS |
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DOI : |
10.3168/jds.2015-10540 |
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Idioma : |
Inglés |
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Notas : |
OPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016 |
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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 |
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Palabras claves : |
FINAL SCORE; GENOMIC EVALUATION; GENOMIC RELATIONSHIP MATRIX. |
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Thesagro : |
SsGBLUP; TORO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12160/1/1-s2.0-S0022030216000825-main.pdf
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Marc : |
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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
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653 $aGENOMIC EVALUATION
653 $aGENOMIC RELATIONSHIP MATRIX
700 1 $aMISZTAL, I.
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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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