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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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| 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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| 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 : |
27/08/2020 |
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Actualizado : |
27/08/2020 |
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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 : |
MACEDO, F.; CHRISTENSEN, O. F.; ASTRUC, J.M.; AGUILAR, I.; MASUDA, Y.; LEGARRA, A. |
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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. |
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Título : |
Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. |
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Fecha de publicación : |
2020 |
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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 |
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ISSN : |
1297-9686 |
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DOI : |
10.1186/s12711-020-00567-1 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 03 March 2020; Accepted 04 August 2020; Published 12 August 2020. |
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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 |
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Palabras claves : |
Animal experiment; Animal model; Dairy sheep; Genetic marker. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
https://gsejournal.biomedcentral.com/track/pdf/10.1186/s12711-020-00567-1
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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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