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| Registros recuperados : 45 | |
| 4. |  | MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I. Acceleration of computations in AI REML for single-step GBLUP models. Volume Methods and Tools: Statistical methods - linear and nonlinear models (Posters), 703. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.703.| Biblioteca(s): INIA Las Brujas. |
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| 6. | | AGUILAR, I.; MISZTAL, I.; LEGARRA, A.; TSURUTA, S. Efficient computations of genomic relationship matrix and other matrices used in the single-step evaluation. Volume Methods and tools: Software and bioinformatics - Lecture Sessions, 0768. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0768. Acknowledgments: This study was partially funded by the Holstein Association USA Inc. and by AFRI grants 2009-65205-05665 and 2010-65205-20366 from the USDA NIFA Animal Genome Program. The authors thank P.M. VanRaden from Animal...| Biblioteca(s): INIA Las Brujas. |
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| 8. | | TSURUTA, S.; MISZTAL, I.; AGUILAR, I.; LAWLOR, T. J. Genome wide association study on cow mortality in three US regions. Volume Species Breeding: Dairy cattle (Posters), 805. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.805.| Biblioteca(s): INIA Las Brujas. |
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| 9. | | TSURUTA, S.; MISZTAL, I.; AGUILAR, I.; LAWLOR, T.J. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science, 2011, v.94, no.8, p.4198-4204. OPEN ACCESS. Article history: Received February 9, 2011. / Accepted April 8, 2011.| Biblioteca(s): INIA Las Brujas. |
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| 10. | | AGUILAR, I.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; WANG, H. PREGSF90 - POSTGSF90: Computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. Volume Methods and Tools: Statistical and genomic tools for mapping QTL and genes (Posters), 680. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.680.| Biblioteca(s): INIA Las Brujas. |
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| 11. | | MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I. Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements. Journal of Animal Science, 2015, v. 93, p. 4670 - 4674. Published October 9, 2015 Article history: Received June 8, 2015.; Accepted August 7, 2015.
1. We acknowledge the work by François Guillaume in programming a hash function. We greatly appreciate the work of the two anonymous reviewers.
2. The AIREMLF90 program...| Biblioteca(s): INIA Las Brujas. |
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| 13. | | LOURENCO, D; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; LAWLOR, T. J.; WELLER, J. I. Are evaluations on young genotyped dairy bulls benefiting from the past generations? [conference paper]. Volume Species Breeding: Dairy cattle, 297. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.297.| Biblioteca(s): INIA Las Brujas. |
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| 14. | | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; MILLER, S.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. Accuracy of indirect predictions for large datasets based on prediction error covariance of SNP effects from single-step GBLUP. [abstract 22]. Issue Section: Animal Breeding and Genetics. Journal of Animal Science, 2020, Volume 98, Issue Supplement 4, Pages 6-7. doi: https://doi.org/10.1093/jas/skaa278.012 Article history: 30 November 2020.
ASAS Annual 2020 Meeting Abstracts.| Biblioteca(s): INIA Las Brujas. |
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| 15. | | AGUILAR, I.; TSURUTA, S.; MASUDA, Y.; LOURENCO, D.A.L.; LEGARRA, A.; MISZTAL, I. BLUPF90 suite of programs for animal breeding with focus on genomics. Volume Methods and Tools - Software, p. 751. 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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| 16. | | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. Correction: Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP (Genetics, selection, evolution : GSE (2022) 54:1 (66)). Genetics, Selection, Evolution : GSE, 2023, Volume 55, Issue 1, Pages 26. OPEN ACCESS. https://doi.org/10.1186/s12711-023-00799-x Article history: Published online 17 April 2023. -- Document: Erratum - Gold Open Access. -- The original article can be found online at https://doi.org/10.1186/s12711-022-00752-4| Biblioteca(s): INIA Las Brujas. |
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| 17. | | 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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| 18. | | AGUILAR, I.; MISZTAL, I.; JOHNSON, D.L.; LEGARRA, A.; TSURUTA, S.; LAWLOR, T.J. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 2010, v. 93, no. 2, p. 743-752. OPEN ACCESS Article history: Received September 14, 2009 / Accepted November 10, 2009 / Published in issue: February 2010.| Biblioteca(s): INIA Las Brujas. |
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| 19. | | MISZTAL, I.; LOURENCO, D.; TSURUTA, S.; AGUILAR, I.; MASUDA, Y.; BERMANN, M.; CESARANI, A.; LEGARRA, A. How ssGBLUP became suitable for national dairy cattle evaluations. [668]. Part 37 - Bovine dairy - genetic evaluation methods. In: Proceedings of the World Congress on Genetics Applied to Livestock Production (WCGALP), 12., Rotterdam, the Netherlands, 3-8 July 2022. doi: https://doi.org/10.3920/978-90-8686-940-4_668 2757-2760. Article history: Published online: February 9, 2023 -- Corresponding author: I. Misztal, email: ignacy@uga.edu| Biblioteca(s): INIA Las Brujas. |
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| 20. | | TSURUTA, S.; AGUILAR, I.; MISZTAL, I.; LEGARRA, A.; LAWLOR, T. J. Multiple trait genetic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Volume Genetic improvement programmes: Selection using molecular information - Poster Sessions, 0489. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0489.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 45 | |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Las Brujas. |
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Fecha actual : |
21/02/2014 |
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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 : |
A - 1 |
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Autor : |
AGUILAR, I.; MISZTAL, I.; TSURUTA, S. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; I. MISZTAL, University of Georgia (US); S. TSURUTA, University of Georgia (US). |
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Título : |
Genetic components of heat stress for dairy cattle with multiple lactations. |
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Fecha de publicación : |
2009 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2009, v. 92, no. 11, p. 5702-5711. |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2008-1928 |
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Idioma : |
Español
Inglés |
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Contenido : |
ABSTRACT.
Data included 585,119 test-day records for milk, fat, and protein yields from the first, second, and third parities of 38,608 Holsteins in Georgia. Daily temperature-humidity indexes (THI) were available from public weather stations. Models included a repeatability test-day model with a random regression on a function of THI and a test-day random regression model using linear splines with knots at 5, 50, 200, and 305 d in milk and a function of THI. Random effects were additive genetic and permanent environmental in the repeatability model and additive genetic, permanent environmental, and herd year in the random regression model. Additionally, models included fixed effects for herd test day, calving age, milking frequency, and lactation stage. Phenotypic variance increased by 50 to 60% from the first to second parity for all yield traits with the repeatability model and by 12 to 15% from the second to third parity. General additive genetic variance increased by 25 to 35% from the first to second parity for all yield traits but decreased slightly from the second to third parity for milk and protein yields. Genetic variance for heat tolerance doubled from the first to second parity and increased by 20 to 100% from the second to third parity. Genetic correlations among general additive effects were lowest between the first and second parities (0.84 to 0.88) and were highest between the second and third parities (0.96 to 0.98). Genetic correlations among parities for the effect of heat tolerance ranged from 0.56 to 0.79. Genetic correlations between general and heat-tolerance effects across parities and yield traits ranged from -0.30 to -0.50. With the random regression model, genetic variance for heat tolerance for milk yield was approximately one-half that of the repeatability model. For milk yield, the most negative genetic correlation (approximately -0.45) between general and heat-tolerance effects was between 50 and 200 d in milk for the first parity and between 200 and 305 d in milk for the second and third parities. The genetic variance of heat tolerance increased substantially from the first to third parity. Genetic estimates of heat tolerance may be inflated with the repeatability model because of timing of lactations to avoid peak yield during hot seasons.
© American Dairy Science Association, 2009. MenosABSTRACT.
Data included 585,119 test-day records for milk, fat, and protein yields from the first, second, and third parities of 38,608 Holsteins in Georgia. Daily temperature-humidity indexes (THI) were available from public weather stations. Models included a repeatability test-day model with a random regression on a function of THI and a test-day random regression model using linear splines with knots at 5, 50, 200, and 305 d in milk and a function of THI. Random effects were additive genetic and permanent environmental in the repeatability model and additive genetic, permanent environmental, and herd year in the random regression model. Additionally, models included fixed effects for herd test day, calving age, milking frequency, and lactation stage. Phenotypic variance increased by 50 to 60% from the first to second parity for all yield traits with the repeatability model and by 12 to 15% from the second to third parity. General additive genetic variance increased by 25 to 35% from the first to second parity for all yield traits but decreased slightly from the second to third parity for milk and protein yields. Genetic variance for heat tolerance doubled from the first to second parity and increased by 20 to 100% from the second to third parity. Genetic correlations among general additive effects were lowest between the first and second parities (0.84 to 0.88) and were highest between the second and third parities (0.96 to 0.98). Genetic correlations among parities for... Presentar Todo |
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Thesagro : |
GANADO DE LECHE; LACTANCIA; MODELOS MATEMÁTICOS; PRODUCCION DE LECHE. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3707/1/Aguilar-I.-2009.-Jr.Dairy-Sc.-v.92-p.5702-5711.pdf
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Marc : |
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245 $aGenetic components of heat stress for dairy cattle with multiple lactations.
260 $c2009
520 $aABSTRACT. Data included 585,119 test-day records for milk, fat, and protein yields from the first, second, and third parities of 38,608 Holsteins in Georgia. Daily temperature-humidity indexes (THI) were available from public weather stations. Models included a repeatability test-day model with a random regression on a function of THI and a test-day random regression model using linear splines with knots at 5, 50, 200, and 305 d in milk and a function of THI. Random effects were additive genetic and permanent environmental in the repeatability model and additive genetic, permanent environmental, and herd year in the random regression model. Additionally, models included fixed effects for herd test day, calving age, milking frequency, and lactation stage. Phenotypic variance increased by 50 to 60% from the first to second parity for all yield traits with the repeatability model and by 12 to 15% from the second to third parity. General additive genetic variance increased by 25 to 35% from the first to second parity for all yield traits but decreased slightly from the second to third parity for milk and protein yields. Genetic variance for heat tolerance doubled from the first to second parity and increased by 20 to 100% from the second to third parity. Genetic correlations among general additive effects were lowest between the first and second parities (0.84 to 0.88) and were highest between the second and third parities (0.96 to 0.98). Genetic correlations among parities for the effect of heat tolerance ranged from 0.56 to 0.79. Genetic correlations between general and heat-tolerance effects across parities and yield traits ranged from -0.30 to -0.50. With the random regression model, genetic variance for heat tolerance for milk yield was approximately one-half that of the repeatability model. For milk yield, the most negative genetic correlation (approximately -0.45) between general and heat-tolerance effects was between 50 and 200 d in milk for the first parity and between 200 and 305 d in milk for the second and third parities. The genetic variance of heat tolerance increased substantially from the first to third parity. Genetic estimates of heat tolerance may be inflated with the repeatability model because of timing of lactations to avoid peak yield during hot seasons. © American Dairy Science Association, 2009.
650 $aGANADO DE LECHE
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