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| Registros recuperados : 223 | |
| 101. |  | LEMA, O.M.; AGUILAR, I.; DIONELLO, N.J.L.; CARDOSO, F.F.; RAVAGNOLO, O.; GIMENO, D. Additive, heterotic and recombination losses for direct and maternal effects in growth for British, Continental and Zebu crosses. 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 La Estanzuela. |
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| 102. | | 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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| 103. | | 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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| 104. | | GROMPONE, M.A.; PAGANO, T.; HARISPE, R.; MIÑOS, A.; BODMER, L.; AGUILAR, I.; GIMENO, D. Contenido y composición lipídica de carnes bovinas uruguayas provenientes de diferentes cruzamientos. C&A Carnes y Alimentos, 2002, v. 3, no. 6, p. 5-9.| Biblioteca(s): INIA Tacuarembó. |
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| 105. | | 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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| 106. | | FERNANDES, A.F.A.; AGUILAR, I.; NEVES, H.H.R.; CARVALHEIRO, R.; QUEIROZ, S.A. Escore de condiçao corporal como indicador de habilidade materna e desempenho futuro de vacas Nelore. [Resumo]. Congreso de la Asociación Latinoamericana de Producción Animal, 24.; Congreso de la Sociedad Chilena de Producción Animal, 40., Puerto Varas, Chile, 9 al 13 noviembre 2015. ln: Reunión ALPA (24., Puerto Varas, Chile). Resúmenes. Puerto Varas (Chile): ALPA, 2015. p. 898| Biblioteca(s): INIA Las Brujas. |
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| 108. | | MISZTAL, I.; AGUILAR, I.; LOURENCO, D.; MA, L.; STEIBEL, J.P. Emerging issues in genomic selection. Animal Genetics and Genomics. Journal of Animal Science, June 2021, Volume 99, Issue 61, skab092. OPEN ACCESS. Doi: https://doi.org/10.1093/jas/skab092 Article history: Received 23 January 2021; Accepted 26 March 2021; Advance Access publication March 27, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License...| Biblioteca(s): INIA Las Brujas. |
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| 109. | | NAVAJAS, E.; RAVAGNOLO, O.; AGUILAR, I.; CIAPPESONI, G.; PERAZA, P.; DALLA RIZZA, M.; MONTOSSI, F. Desarrollo de una plataforma en selección genómica enfocada en el progreso genético animal. ln: Jornada técnica, VI Jornada de agrobiotecnología. INIA Las Brujas, 20 de octubre de 2012 Conocimiento intensivo para el sector productivo: situación actual y perspectivas. Canelones (UY): INIA, 2012. 10-12 (Serie Actividades de Difusión; 698)| Biblioteca(s): INIA Las Brujas. |
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| 110. | | 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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| 111. | | 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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| 112. | | CHEN, C.Y.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; MUIR, W.M. Effect of different genomic relationship matrices on accuracy and scale. Journal of Animal Science, 2011, v.89, no.9, p.2673-2679. Article history: Received September 29, 2010. / Accepted March 21, 2011.| Biblioteca(s): INIA Las Brujas. |
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| 113. | | AGUILAR, I.; FERNÁNDEZ, E.N.; BLASCO, A.; RAVAGNOLO, O.; LEGARRA, A. Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP. Journal of Animal Breeding and Genetics, 1 July 2020, Volume 137, Issue 4, pp. 356-364. Doi: https://doi.org/10.1111/jbg.12470 Article history: Received: 22 August 2019 / Revised: 10 December 2019 / Accepted: 11 January 2020 / First published:20 February 2020
Corresponding author: Aguilar, I.; email:iaguilar@inia.org.uy
Funding information: FEDER; INRA;...| Biblioteca(s): INIA Las Brujas. |
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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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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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