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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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INIA Las Brujas (LB) |
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| 1. |  | RIOS, A.; IBARRA, M.; ROTH, Y. Control de campo sucio en sistemas pastoriles. ln: CONGRESO NACIONAL DE INGENIERÍA AGRONÓMICA, 7.; JORNADA DE SIEMBRA DIRECTA, 1997, MONTEVIDEO, UY. Compendio de trabajos presentados. Montevideo, UY: AIA, 1997. p. 143-144.| Tipo: Trabajos en Congresos/Conferencias |
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