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Registros recuperados : 224 | |
81. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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. | ![Imagen marcada / sin marcar](/consulta/web/img/desmarcado.png) | 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 : |
27/08/2020 |
Actualizado : |
27/08/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
MACEDO, F.; CHRISTENSEN, O. F.; ASTRUC, J.M.; AGUILAR, I.; MASUDA, Y.; LEGARRA, A. |
Afiliación : |
FERNANDO LIBER MACEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GenPhySE, Castanet Tolosan, France; OLE F. CHRISTENSEN, Center for Quantitative Genetics and Genomics, Tjele, Denmark; JEAN-MICHEL ASTRUC, Institut de l’Elevage, Castanet Tolosan, France; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; YUTAKA MASUDA, Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA; ANDRÉS LEGARRA, GenPhySE, INRAE, Castanet Tolosan, France. |
Título : |
Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
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 |
ISSN : |
1297-9686 |
DOI : |
10.1186/s12711-020-00567-1 |
Idioma : |
Inglés |
Notas : |
Article history: Received 03 March 2020; Accepted 04 August 2020; Published 12 August 2020. |
Contenido : |
Abstract
BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. CONCLUSIONS: The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years. MenosAbstract
BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of... Presentar Todo |
Palabras claves : |
Animal experiment; Animal model; Dairy sheep; Genetic marker. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
https://gsejournal.biomedcentral.com/track/pdf/10.1186/s12711-020-00567-1
|
Marc : |
LEADER 03420naa a2200265 a 4500 001 1061282 005 2020-08-27 008 2020 bl uuuu u00u1 u #d 022 $a1297-9686 024 7 $a10.1186/s12711-020-00567-1$2DOI 100 1 $aMACEDO, F. 245 $aBias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.$h[electronic resource] 260 $c2020 500 $aArticle history: Received 03 March 2020; Accepted 04 August 2020; Published 12 August 2020. 520 $aAbstract BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. CONCLUSIONS: The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years. 653 $aAnimal experiment 653 $aAnimal model 653 $aDairy sheep 653 $aGenetic marker 700 1 $aCHRISTENSEN, O. F. 700 1 $aASTRUC, J.M. 700 1 $aAGUILAR, I. 700 1 $aMASUDA, Y. 700 1 $aLEGARRA, A. 773 $tGenetics, Selection, Evolution : GSE, 12 August 2020, Volume 52, Issue 1, Page 47. OPEN ACCESS. DOI: https://doi.org/10.1186/s12711-020-00567-1
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