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Biblioteca(s): | INIA Las Brujas.
Fecha corriente: | 07/10/2022
Fecha de la última actualización: | 27/04/2023
Autoria: | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D.
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.
Título: | Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP.
Año de publicación: | 2022
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
ISSN: | 1297-9686
DOI: | 10.1186/s12711-022-00752-4
Idioma: | Inglés
Notas: | Article history: Received 22 March 2022; Accepted 23 August 2022; Published 27 September 2022.
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).
Palabras-Clave: | Algorithm; Breeding; Covariance; Prediction error; Single nucleotide polymorphism.
Categoría del tema: | L10 Genética y mejoramiento animal

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

Registro original: | INIA Las Brujas (LB)
Ejemplares: | LB - (103218-1) - PXI - AP - DD - PP/GSE/2022 -  -  - DIS