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Registros recuperados : 224 | |
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.62Biblioteca(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. |
Fecha actual : |
15/11/2015 |
Actualizado : |
09/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
B - 2 |
Autor : |
FRAGOMENI, B.O.; MISZTAL, I.; LOURENCO, D.L.; AGUILAR, I.; OKIMOTO, R.; MUIR, W.M. |
Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Changes in variance explained by top SNP windows over generations for three traits in broiler chicken |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
Frontiers in Genetics, 2014, v.5, no.Oct., Article number 332. OPEN ACCESS. |
ISSN : |
1664-8021 |
DOI : |
10.3389/fgene.2014.00332 |
Idioma : |
Inglés |
Notas : |
Article history: Published 01 October 2014. |
Contenido : |
ABSTRACT.
The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.
© 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and Muir. MenosABSTRACT.
The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.
© 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and... Presentar Todo |
Palabras claves : |
Gene identification; Genome-wide association study; Genomic selection; QTL; SsGBLUP. |
Thesagro : |
MEJORAMIENTO GENETICO ANIMAL; POLLO DE ENGORDE. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5195/1/Aguilar-I.-2014.-Frontiers-in-Genetics.pdf
|
Marc : |
LEADER 02443naa a2200301 a 4500 001 1053891 005 2019-10-09 008 2014 bl uuuu u00u1 u #d 022 $a1664-8021 024 7 $a10.3389/fgene.2014.00332$2DOI 100 1 $aFRAGOMENI, B.O. 245 $aChanges in variance explained by top SNP windows over generations for three traits in broiler chicken$h[electronic resource] 260 $c2014 500 $aArticle history: Published 01 October 2014. 520 $aABSTRACT. The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated. © 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and Muir. 650 $aMEJORAMIENTO GENETICO ANIMAL 650 $aPOLLO DE ENGORDE 653 $aGene identification 653 $aGenome-wide association study 653 $aGenomic selection 653 $aQTL 653 $aSsGBLUP 700 1 $aMISZTAL, I. 700 1 $aLOURENCO, D.L. 700 1 $aAGUILAR, I. 700 1 $aOKIMOTO, R. 700 1 $aMUIR, W.M. 773 $tFrontiers in Genetics, 2014$gv.5, no.Oct., Article number 332. OPEN ACCESS.
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