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| Registros recuperados : 33 | |
| 1. |  | BERMANN, M.; MISZTAL, I.; LOURENCO, D.; AGUILAR, I.; LEGARRA, A. Definition of reliabilities for models with metafounders. [289] Part 17 - Challenges - improving genomic prediction. 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_289 1217-1220. Article history: Published online: February 9, 2023. -- Corresponding author: A. Legarra, email: andres.legarra@inrae.fr -- Acknowledgment: This work received financing from European Unions' Horizon 2020 Research & Innovation Programme,...| Biblioteca(s): INIA Las Brujas. |
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| 2. | | BERMANN, M.; AGUILAR, I.; LOURENCO , D.; MISZTAL, I.; LEGARRA, A. Reliabilities of estimated breeding values in models with metafounders. Research article. Genetics, Selection, Evolution : GSE, 2023, volume55, issue 1, article 6. OPEN ACCESS. doi: https://doi.org/10.1186/s12711-023-00778-2 Article history: Received 29 June 2022; Accepted 04 January 2023; Published 23 January 2023. -- Corresponding author: Matias Bermann, Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA, email: mbermann@uga.edu...| Biblioteca(s): INIA Las Brujas. |
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| 3. | | ZHANG, X.; LOURENCO, D.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A. Weighted single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Volume Methods and Tools: Statistical and genomic tools for mapping QTL and genes (Posters), 681. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.681.| Biblioteca(s): INIA Las Brujas. |
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| 4. | | ZHANG, X.; LOURENCO, D.; AGUILAR, I.; LEGARRA, A.; MISZTAL, I. Weighting strategies for single-step genomic BLUP: An iterative approach for accurate calculation of GEBV and GWAS. Frontiers in Genetics, 19 August 2016, Volume 7, Issue AUG, Article number 151. OPEN ACCESS Article history: Received 15 May 2016 // Accepted 04 August 2016 // Published 19 August 2016.
Specialty section:
This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics.| Biblioteca(s): INIA Las Brujas. |
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| 5. | | 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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| 6. | | 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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| 7. | | 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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| 8. | | 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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| 9. | | 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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| 10. | | 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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| 11. | | 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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| 12. | | 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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| 13. | | 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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| 14. | | LOURENCO, D.A.L.; MISZTAL, I.; WANG, H.; AGUILAR, I.; TSURUTA, S.; BERTRAND, J.K. Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models. Journal of Animal Science, 2013, v.91, no.9, p.4090-4098. Article history: Published online July 26, 2013.
This study was partially funded by the American Angus Association (St. Joseph, MO) and the USDA Agriculture and Food Research Initiative (Grant no. 2009-65205-05665 from the USDA National...| Biblioteca(s): INIA Las Brujas. |
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| 15. | | 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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| 16. | | 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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| 17. | | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP. Genetics, Selection, Evolution : GSE, 2022, Volume 54, Issue 1, Pages 66. OPEN ACCESS. doi: https://doi.org/10.1186/s12711-022-00752-4 Article history: Received 22 March 2022; Accepted 23 August 2022; Published 27 September 2022.| Biblioteca(s): INIA Las Brujas. |
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| 18. | | 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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| 19. | | LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; LAWLOR, T.J.; FORNI, S.; WELLER, J.I. Are evaluations on young genotyped animals benefiting from the past generations?. Journal of Dairy Science, 2014, v.97, no.6, p.3930-3942. OPEN ACCESS Article history: Received November 26, 2013. // Accepted February 11, 2014. OPEN ACCESS| Biblioteca(s): INIA Las Brujas. |
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| 20. | | 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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| Registros recuperados : 33 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
30/06/2021 |
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Actualizado : |
30/06/2021 |
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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 : |
MISZTAL, I.; AGUILAR, I.; LOURENCO, D.; MA, L.; STEIBEL, J.P. |
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Afiliación : |
IGNACY MISZTAL, Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIELA LOURENCO, Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.; LI MA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.; JUAN PEDRO STEIBEL, Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA. |
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Título : |
Emerging issues in genomic selection. |
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Complemento del título : |
Animal Genetics and Genomics. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Journal of Animal Science, June 2021, Volume 99, Issue 61, skab092. OPEN ACCESS. Doi: https://doi.org/10.1093/jas/skab092 |
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ISSN : |
1525-3163 |
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DOI : |
10.1093/jas/skab092 |
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Idioma : |
Inglés |
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Notas : |
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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
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Contenido : |
ABSTRACT. - Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. MenosABSTRACT. - Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for import... Presentar Todo |
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Palabras claves : |
Genomic evaluation; Genomic selection; Genomwide association studies; Large data; Stability of genomic predictions. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
https://academic.oup.com/jas/article-pdf/99/6/skab092/38539545/skab092.pdf
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Marc : |
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022 $a1525-3163
024 7 $a10.1093/jas/skab092$2DOI
100 1 $aMISZTAL, I.
245 $aEmerging issues in genomic selection.$h[electronic resource]
260 $c2021
500 $aArticle 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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
520 $aABSTRACT. - Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface. © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science.
653 $aGenomic evaluation
653 $aGenomic selection
653 $aGenomwide association studies
653 $aLarge data
653 $aStability of genomic predictions
700 1 $aAGUILAR, I.
700 1 $aLOURENCO, D.
700 1 $aMA, L.
700 1 $aSTEIBEL, J.P.
773 $tJournal of Animal Science, June 2021, Volume 99, Issue 61, skab092. OPEN ACCESS. Doi: https://doi.org/10.1093/jas/skab092
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