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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.edu| Biblioteca(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.edu| Biblioteca(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 2016| Biblioteca(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. |
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Fecha actual : |
04/07/2019 |
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Actualizado : |
04/07/2019 |
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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 : |
AGUILAR, I.; LEGARRA, A.; CARDOSO, F.; MASUDA, Y.; LOURENCO, D.; MISZTAL, I. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA (Institut National de la Recherche Agronomique); FERNANDO CARDOSO, Department of Animal Science, Federal University of Pelotas, Brazil; Embrapa Pecuária Sul, Brazil; YUTAKA MASUDA, Department of Animal and Dairy Science, University of Georgia, United States; DANIELA LOURENCO, Department of Animal and Dairy Science, University of Georgia, United States; IGNACY MISZTAL, Department of Animal and Dairy Science, University of Georgia, United States. |
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Título : |
Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. (Short Communication) |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Genetics Selection Evolution, 20 June 2019, v. 51, Issue 1, Article number 28. OPEN ACCESS. |
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ISSN : |
0999-193X |
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DOI : |
10.1186/s12711-019-0469-3 |
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Idioma : |
Inglés |
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Notas : |
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 Initiative Competitive Grants No. 2015-67015-22936 from the US Department of Agriculture?s National Institute of Food and Agriculture.
Availability of data and materials: The data that support the fndings of this study were provided from the American Angus Association but restrictions apply to the availability of these data, which were used under license for the current study, and thus are not publicly available. The methods described here are included using ?OPTION snp_p_value? in the parameter fle in software blupf90 (factorization of the mixed model equations and solving of the SSGBLUP equations) and postGSf90
(backsolving of snp efects and computation of p-values), available at http://nce.ads.uga.edu/software/. |
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Contenido : |
ABSTRACT.
Background: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. Methods: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. Results: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. Conclusions: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.
© 2019 The Author(s). MenosABSTRACT.
Background: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. Methods: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. Results: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. Conclusions: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of... Presentar Todo |
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Palabras claves : |
ANIMALIA. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12994/1/s12711-019-0469-3.pdf
https://gsejournal.biomedcentral.com/track/pdf/10.1186/s12711-019-0469-3
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Marc : |
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022 $a0999-193X
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100 1 $aAGUILAR, I.
245 $aFrequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle. (Short Communication)$h[electronic resource]
260 $c2019
500 $aArticle 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 Initiative Competitive Grants No. 2015-67015-22936 from the US Department of Agriculture?s National Institute of Food and Agriculture. Availability of data and materials: The data that support the fndings of this study were provided from the American Angus Association but restrictions apply to the availability of these data, which were used under license for the current study, and thus are not publicly available. The methods described here are included using ?OPTION snp_p_value? in the parameter fle in software blupf90 (factorization of the mixed model equations and solving of the SSGBLUP equations) and postGSf90 (backsolving of snp efects and computation of p-values), available at http://nce.ads.uga.edu/software/.
520 $aABSTRACT. Background: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. Methods: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. Results: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. Conclusions: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped. © 2019 The Author(s).
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773 $tGenetics Selection Evolution, 20 June 2019$gv. 51, Issue 1, Article number 28. OPEN ACCESS.
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