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Registros recuperados : 17 | |
1. | | 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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2. | | 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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3. | | MACEDO, F.; CHRISTENSEN, O. F.; ASTRUC, J.M.; AGUILAR, I.; MASUDA, Y.; LEGARRA, A. Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. Genetics, Selection, Evolution : GSE, 12 August 2020, Volume 52, Issue 1, Page 47. OPEN ACCESS. DOI: https://doi.org/10.1186/s12711-020-00567-1 Article history: Received 03 March 2020; Accepted 04 August 2020; Published 12 August 2020.Biblioteca(s): INIA Las Brujas. |
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4. | | 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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5. | | 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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6. | | AGUILAR, I.; LEGARRA, A.; CARDOSO, F.; MASUDA, Y.; LOURENCO, D.; MISZTAL, I. Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. (Short Communication) Genetics Selection Evolution, 20 June 2019, v. 51, Issue 1, Article number 28. OPEN ACCESS. Article history: Received: 3 January 2019 // Accepted: 27 May 2019 // Published Online: 20 June 2019.
Funding text: This study was partially funded by the American Angus Association (St. Joseph, MO) and by Agriculture and Food Research...Biblioteca(s): INIA Las Brujas. |
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7. | | 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.eduBiblioteca(s): INIA Las Brujas. |
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8. | | LOURENCO, D.; TSURUTA, S.; AGUILAR, I.; MASUDA, Y.; BERMANN, M.; LEGARRA, A.; MISZTAL, I. Recent updates in the BLUPF90 software suite. [366]. Part 19 - Methods and tools: software and computing strategies. 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_366 1530-1533. Article history: Published online: February 9, 2023. -- Corresponding author: D. Lourenco, email: danilino@uga.eduBiblioteca(s): INIA Las Brujas. |
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9. | | LOURENCO, D.; LEGARRA, A.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; MISZTAL, I. Single-step genomic evaluations from theory to practice: using snp chips and sequence data in blupf90. Genes, July 2020. Volume 11, Issue 7, Article number 790, Pages 1-32. Open Access. Doi: https://doi.org/10.3390/genes11070790 Article history: Received: 19 June 2020 / Revised: 3 July 2020 / Accepted: 6 July 2020 / Published: 14 July 2020.
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data):...Biblioteca(s): INIA Las Brujas. |
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10. | | 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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11. | | 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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12. | | 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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13. | | 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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14. | | 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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15. | | 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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16. | | 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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17. | | 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 : 17 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
31/03/2021 |
Actualizado : |
31/03/2021 |
Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
Autor : |
LOURENCO, D.; TSURUTA, S.; FRAGOMENI, B.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; MILLER, S.; MOSER, D.; MISZTAL, I. |
Afiliación : |
DANIELA LOURENCO, University of Georgia, Department of Animal and Dairy Science, GA, USA; SHOGO TSURUTA, University of Georgia, Department of Animal and Dairy Science, GA, USA; BRENO FRAGOMENI, University of Georgia, Department of Animal and Dairy Science, GA, USA; YUTAKA MASUDA, University of Georgia, Department of Animal and Dairy Science, GA, USA; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, Institut National de la Recherche Agronomique, Castanet Tolosan, France; STEPHEN MILLER, Angus Genetics Inc., MO, USA; DAN MOSER, Angus Genetics Inc., MO, USA; IGNACY MISZTAL, University of Georgia, Department of Animal and Dairy Science, GA, USA. |
Título : |
Single-step genomic BLUP for national beef cattle evaluation in US: from initial developments to final implementation. |
Complemento del título : |
Volume Species - Bovine (beef) 1, 495. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018. |
Idioma : |
Inglés |
Contenido : |
ABSTRACT.
The objective of this study was to implement single-step genomic BLUP (ssGBLUP) for national Angus cattle evaluation in the US. National evaluations include a variety of models with several linear and categorical traits, maternal effects, multibreed data, and a large number of genotyped animals. For the initial investigation, we used a dataset from 2014 that comprised over 8 million animals, 6 million birth weight (BW) and weaning weight (WW) records, 3.4 million post-weaning gain (PWG) records, and genotypes for 52k animals. A dataset from 2017 was later used that included 335k genotyped animals. The ability to predict future performance of young animals was investigated when using regular BLUP and ssGBLUP. Because of the increasing number of genotyped animals and the high computing cost to invert the genomic relationship matrix (G), the algorithm for proven and young (APY) was used to approximate the inverse of G. The APY uses recursions on a small subset of genotyped animals, called core. We further tested the feasibility of having daily interim genomic predictions for newly-genotyped animals based on SNP effects derived from the previous official ssGBLUP evaluation. In addition, we extended all models used in traditional evaluations to ssGBLUP, and compared genetic trends from traditional BLUP, ssGBLUP, and a multistep method that was implemented for the American Angus genomic evaluation in 2009. A new algorithm to approximate accuracy of GEBV for large genomic data was also developed. On average, the increase in ability to predict future performance, for BW, WW, and PWG, with ssGBLUP was 25% in the 2014 data and 36% in the 2017 data, compared to the traditional BLUP. The ssGBLUP with APY was as accurate as the regular ssGBLUP when the number of core animals was at least 10,000, independently of which animals were in the core group. Interim predictions derived from ssGBLUP provided accurate genomic values for newly-genotyped animals. Genetic trends for ssGBLUP and BLUP were similar, revealing overestimation in multistep evaluations, especially for traits with less phenotypes. Single-step GBLUP became a reality for American Angus evaluation and its implementation process resulted in successful updates in methodology, making this approach mature for national beef cattle evaluation. Keywords: algorithm for proven and young, Angus, genomic selection, indirect prediction. MenosABSTRACT.
The objective of this study was to implement single-step genomic BLUP (ssGBLUP) for national Angus cattle evaluation in the US. National evaluations include a variety of models with several linear and categorical traits, maternal effects, multibreed data, and a large number of genotyped animals. For the initial investigation, we used a dataset from 2014 that comprised over 8 million animals, 6 million birth weight (BW) and weaning weight (WW) records, 3.4 million post-weaning gain (PWG) records, and genotypes for 52k animals. A dataset from 2017 was later used that included 335k genotyped animals. The ability to predict future performance of young animals was investigated when using regular BLUP and ssGBLUP. Because of the increasing number of genotyped animals and the high computing cost to invert the genomic relationship matrix (G), the algorithm for proven and young (APY) was used to approximate the inverse of G. The APY uses recursions on a small subset of genotyped animals, called core. We further tested the feasibility of having daily interim genomic predictions for newly-genotyped animals based on SNP effects derived from the previous official ssGBLUP evaluation. In addition, we extended all models used in traditional evaluations to ssGBLUP, and compared genetic trends from traditional BLUP, ssGBLUP, and a multistep method that was implemented for the American Angus genomic evaluation in 2009. A new algorithm to approximate accuracy of GEBV for large genomic... Presentar Todo |
Palabras claves : |
Algorithm for proven and young; Angus; Genomic selection; Indirect prediction. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15436/1/Lourenco-et-al-2018-WCGALP.pdf
|
Marc : |
LEADER 03286nam a2200253 a 4500 001 1061912 005 2021-03-31 008 2018 bl uuuu u01u1 u #d 100 1 $aLOURENCO, D. 245 $aSingle-step genomic BLUP for national beef cattle evaluation in US$bfrom initial developments to final implementation.$h[electronic resource] 260 $aIn: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018.$c2018 520 $aABSTRACT. The objective of this study was to implement single-step genomic BLUP (ssGBLUP) for national Angus cattle evaluation in the US. National evaluations include a variety of models with several linear and categorical traits, maternal effects, multibreed data, and a large number of genotyped animals. For the initial investigation, we used a dataset from 2014 that comprised over 8 million animals, 6 million birth weight (BW) and weaning weight (WW) records, 3.4 million post-weaning gain (PWG) records, and genotypes for 52k animals. A dataset from 2017 was later used that included 335k genotyped animals. The ability to predict future performance of young animals was investigated when using regular BLUP and ssGBLUP. Because of the increasing number of genotyped animals and the high computing cost to invert the genomic relationship matrix (G), the algorithm for proven and young (APY) was used to approximate the inverse of G. The APY uses recursions on a small subset of genotyped animals, called core. We further tested the feasibility of having daily interim genomic predictions for newly-genotyped animals based on SNP effects derived from the previous official ssGBLUP evaluation. In addition, we extended all models used in traditional evaluations to ssGBLUP, and compared genetic trends from traditional BLUP, ssGBLUP, and a multistep method that was implemented for the American Angus genomic evaluation in 2009. A new algorithm to approximate accuracy of GEBV for large genomic data was also developed. On average, the increase in ability to predict future performance, for BW, WW, and PWG, with ssGBLUP was 25% in the 2014 data and 36% in the 2017 data, compared to the traditional BLUP. The ssGBLUP with APY was as accurate as the regular ssGBLUP when the number of core animals was at least 10,000, independently of which animals were in the core group. Interim predictions derived from ssGBLUP provided accurate genomic values for newly-genotyped animals. Genetic trends for ssGBLUP and BLUP were similar, revealing overestimation in multistep evaluations, especially for traits with less phenotypes. Single-step GBLUP became a reality for American Angus evaluation and its implementation process resulted in successful updates in methodology, making this approach mature for national beef cattle evaluation. Keywords: algorithm for proven and young, Angus, genomic selection, indirect prediction. 653 $aAlgorithm for proven and young 653 $aAngus 653 $aGenomic selection 653 $aIndirect prediction 700 1 $aTSURUTA, S. 700 1 $aFRAGOMENI, B. 700 1 $aMASUDA, Y. 700 1 $aAGUILAR, I. 700 1 $aLEGARRA, A. 700 1 $aMILLER, S. 700 1 $aMOSER, D. 700 1 $aMISZTAL, I.
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