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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
07/10/2022 |
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Actualizado : |
27/04/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. |
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Afiliación : |
ANDRÉ GARCÍA, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA Toulouse, Castanet Tolosan, 31326, France; SHOGO TSURUTA, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States; IGNACY MISZTAL, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States; DANIELA LOURENCO, Department of Animal and Dairy Science, University of Georgia, Athens, 30602, GA, United States. |
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Título : |
Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Genetics, Selection, Evolution : GSE, 2022, Volume 54, Issue 1, Pages 66. OPEN ACCESS. doi: https://doi.org/10.1186/s12711-022-00752-4 |
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ISSN : |
1297-9686 |
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DOI : |
10.1186/s12711-022-00752-4 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 22 March 2022; Accepted 23 August 2022; Published 27 September 2022. |
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Contenido : |
ABSTRACT. - BACKGROUND: Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies. © 2022. The Author(s). |
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Palabras claves : |
Algorithm; Breeding; Covariance; Prediction error; Single nucleotide polymorphism. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16814/1/s12711-022-00752-4.pdf
https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-022-00752-4.pdf
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Marc : |
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008 2022 bl uuuu u00u1 u #d
022 $a1297-9686
024 7 $a10.1186/s12711-022-00752-4$2DOI
100 1 $aGARCÍA, A.
245 $aTheoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 22 March 2022; Accepted 23 August 2022; Published 27 September 2022.
520 $aABSTRACT. - BACKGROUND: Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies. © 2022. The Author(s).
653 $aAlgorithm
653 $aBreeding
653 $aCovariance
653 $aPrediction error
653 $aSingle nucleotide polymorphism
700 1 $aAGUILAR, I.
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aMISZTAL, I.
700 1 $aLOURENCO, D.
773 $tGenetics, Selection, Evolution : GSE, 2022, Volume 54, Issue 1, Pages 66. OPEN ACCESS. doi: https://doi.org/10.1186/s12711-022-00752-4
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
29/03/2021 |
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Actualizado : |
31/03/2021 |
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Tipo de producción científica : |
Trabajos en Congresos/Conferencias |
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Autor : |
RAVAGNOLO, O.; AGUILAR, I.; CROWLEY, J. J.; PRAVIA, M.I.; LEMA, O.M.; MACEDO, F.; SCOTT, S.; NAVAJAS, E. |
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Afiliación : |
OLGA RAVAGNOLO GUMILA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; J.J. CROWLEY, Canadian Beef Breeds Council, Calgary, Alberta, Canada; Livestock Gentec, University of Alberta, Edmonton, Canada; MARIA ISABEL PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OSCAR MARIO LEMA QUEIJO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO LIBER MACEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; S. SCOTT, Canadian Hereford Association, Calgary, Alberta, Canada; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Accuracy of genomic predictions of residual feed intake in Hereford with Uruguayan and Canadian training populations. |
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Complemento del título : |
Volume: Electronic Poster Session - Species - Bovine (beef) 1, p. 723. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018. |
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Páginas : |
6 p. |
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Idioma : |
Inglés |
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Contenido : |
SUMMARY.
Dataset from Canadian and Uruguayan training populations were joined to analyse improvement of predictability for RFI. Three training populations where defined, URY (only data from Uruguay, 731), CAN (only data from Canada, 1168) and TOTAL (joint dataset, 1899). Genealogical information from the two countries was merged based on the international identification and cross reference list, with the pedigree file resulting in 17289 animals.
The demands for livestock products are increasing, and beef production seems not to be an exception. This implies a challenge to beef production that has to increase productivity without increasing area or environmental footprint (a finite commodity), increasing costs (competing in disadvantage with chicken and pigs) or lowering product quality (its main advantage).
The objective of present study was to compare the accuracy of genomic predictions for RFI based on national and bi-national training populations. |
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Palabras claves : |
Accuracy; Beef cattle; BEEF PRODUCTION; Feed efficiency; Genomic selection; Training population. |
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Thesagro : |
ALIMENTACION ANIMAL; GANADO DE CARNE. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15398/1/accuracy-genomic-predictions-residual-feed-intake-hereford-uruguayan-and-canadian-training.pdf
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Marc : |
LEADER 01939nam a2200301 a 4500
001 1061876
005 2021-03-31
008 2018 bl uuuu u01u1 u #d
100 1 $aRAVAGNOLO, O.
245 $aAccuracy of genomic predictions of residual feed intake in Hereford with Uruguayan and Canadian training populations.$h[electronic resource]
260 $aIn: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018.$c2018
300 $a6 p.
520 $aSUMMARY. Dataset from Canadian and Uruguayan training populations were joined to analyse improvement of predictability for RFI. Three training populations where defined, URY (only data from Uruguay, 731), CAN (only data from Canada, 1168) and TOTAL (joint dataset, 1899). Genealogical information from the two countries was merged based on the international identification and cross reference list, with the pedigree file resulting in 17289 animals. The demands for livestock products are increasing, and beef production seems not to be an exception. This implies a challenge to beef production that has to increase productivity without increasing area or environmental footprint (a finite commodity), increasing costs (competing in disadvantage with chicken and pigs) or lowering product quality (its main advantage). The objective of present study was to compare the accuracy of genomic predictions for RFI based on national and bi-national training populations.
650 $aALIMENTACION ANIMAL
650 $aGANADO DE CARNE
653 $aAccuracy
653 $aBeef cattle
653 $aBEEF PRODUCTION
653 $aFeed efficiency
653 $aGenomic selection
653 $aTraining population
700 1 $aAGUILAR, I.
700 1 $aCROWLEY, J. J.
700 1 $aPRAVIA, M.I.
700 1 $aLEMA, O.M.
700 1 $aMACEDO, F.
700 1 $aSCOTT, S.
700 1 $aNAVAJAS, E.
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