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| 81. |  | NEGRI, R.; AGUILAR, I.; FELTES, G. L.; COBUCI, J. A. Selection for test-day milk yield and thermotolerance in brazilian holstein cattle. Animals, January 2021, Volume 11, Issue 1, Article number 128, Pages 1-13. OPEN ACCESS. Doi: https://doi.org/10.3390/ani11010128 Article history: Received 16 November 2020; Accepted 29 December 2020; Published 8 January 2021.
Corresponding author: Negri, R.; Department of Animal Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil;...| Biblioteca(s): INIA Las Brujas. |
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| 83. | | 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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| 87. | | 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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| 88. | | 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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| 92. | | 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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| 95. | | LEMA, O.M.; BRITO, G.; CLARIGET, J.; PEREZ, E.; RAVAGNOLO, O.; AGUILAR, I.; MONTOSSI, F. Dos años de evaluación de ganancia diaria invernal de terneros con paternidad conocida y su efecto sobre la recría
y terminación. In: CONGRESO ARGENTINO DE PRODUCCIÓN ANIMAL, 38., 2015. Resúmenes. Santa Rosa, La Pampa, AR: ASAS/AAPA, 2015 Revista Argentina de Producción Animal, 2015, v.35, Supl.1, p.62| Biblioteca(s): INIA La Estanzuela; INIA Treinta y Tres. |
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| 98. | | NAVAJAS, E.; MACEDO, F.; LEMA, O.M.; LUZARDO, S.; AGUILAR, I. Accuracy of genomic predictions for carcass and meat quality traits in the Uruguayan Hereford breed. Volume Species - Bovine (beef) 1, p. 636. 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. Acknowledgements: This work was supported by the Agencia Nacional de Investigación e Innovación (ANII) (grants RTS_1_2012_1_3489 and FMV_1_2011_1_6671), Instituto Nacional de Investigación Agropecuaria (INIA), Sociedad de Criadores de...| Biblioteca(s): INIA Las Brujas. |
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| 99. | | 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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| 100. | | LEGARRA, A.; CHRISTENSEN, O. F.; VITEZICA, Z. G.; AGUILAR, I.; MISZTAL, I. Across-breeds ancestral relationships and metafounders for genomic evaluation. Volume Genetic Improvement Programs: Selection using molecular information, 075. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.075. Acknowledgements: This project has been financed by X-Gen and GenSSeq actions from SelGen metaprogram (INRA). We are grateful to the genotoul bioinformatics platform Toulouse Midi-Pyrenees for providing computing resources.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 224 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
11/12/2018 |
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Actualizado : |
11/12/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 2 |
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Autor : |
ZHANG, X.; LOURENCO, D.; AGUILAR, I.; LEGARRA, A.; MISZTAL, I. |
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Afiliación : |
XINYUE ZHANG, Animal and Dairy Science, Animal Breeding and Genetics, University of Georgia, United States; DANIELA LOURENCO, Animal and Dairy Science, Animal Breeding and Genetics, University of Georgia, United States; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA (Institut National de la Recherche Agronomique); IGNACY MISZTAL, Animal and Dairy Science, Animal Breeding and Genetics, University of Georgia, United States. |
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Título : |
Weighting strategies for single-step genomic BLUP: An iterative approach for accurate calculation of GEBV and GWAS. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Frontiers in Genetics, 19 August 2016, Volume 7, Issue AUG, Article number 151. OPEN ACCESS |
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ISSN : |
1664-8021 |
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DOI : |
10.3389/fgene.2016.00151 |
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Idioma : |
Inglés |
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Notas : |
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. |
|
Contenido : |
ABSTRACT.
Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u2) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects.
© 2016 Zhang, Lourenco, Aguilar, Legarra and Misztal. MenosABSTRACT.
Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u2) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssG... Presentar Todo |
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Palabras claves : |
BayesB; BayesC; GENOME-WIDE ASSOCIATION; SNP WINDOW; WssGBLUP. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12161/1/fgene-07-00151.pdf
https://www.frontiersin.org/articles/10.3389/fgene.2016.00151/full
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Marc : |
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520 $aABSTRACT. Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u2) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects. © 2016 Zhang, Lourenco, Aguilar, Legarra and Misztal.
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773 $tFrontiers in Genetics, 19 August 2016, Volume 7, Issue AUG, Article number 151. OPEN ACCESS
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