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A comparison of methods to estimate genomic relationships using pedigree and markers in livestock populations

Colaborador(es): Forneris, Natalia Soledad. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina | Steibel, Juan Pedro. Michigan State University. Department of Animal Science. East Lansing, Michigan, USA | Legarra, Andres. INRA, Gen PhySE (Genetique, Physiologie et Systemes d’Elevage), Castanet-Tolosan, France | Vitezica, Zulma Gladis. INRA, Gen PhySE (Genetique, Physiologie et Systemes d’Elevage), Castanet-Tolosan, France. INP, ENSAT, GenPhySE (Genetique, Physiologie et Systemes d’Elevage), Universite de Toulouse, Castanet-Tolosan, France | Bates, R. O. Michigan State University. Department of Animal Science. East Lansing, MichiganI, USA | Ernst, C. W. Michigan State University. Department of Animal Science. East Lansing, Michigan, USA | Basso, Alicia Leonor. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Biología Aplicada y Alimentos. Buenos Aires, Argentina | Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA). Buenos Aires, Argentina. CONICET – Universidad de Buenos Aires. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales (INBA). Buenos Aires, Argentina.
ISSN: 0931-2668.Tipo de material: Artículos y capítulos. Recurso electrónico.Tema(s): ACCURACY | GENOMIC PREDICTION | GENOME SHARING | IDENTITY BY DESCENT | SNP | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR En: Journal of animal breeding and genetics Vol.133, no.6 (2016), p.452-462, tbls.Resumen: Accurate prediction of breeding values depends on capturing the variability in genome sharing of relatives with the same pedigree relationship. Here, we compare two approaches to set up genomic relationship matrices for precision of genomic relationships (GR) and accuracy of estimated breeding values (GEBV). Real and simulated data (pigs, 60k SNP) were analysed, and GR were estimated using two approaches: (i) identity by state, corrected with either the observed (GVR-O) or the base population (GVR-B) allele frequencies and (ii) identity by descent using linkage analysis (GIBD-L). Estimators were evaluated for precision and empirical bias with respect to true pedigree IBD GR. All three estimators had very low bias. GIBD-L displayed the lowest sampling error and the highest correlation with true genome-shared values. GVR-B approximated GIBD-L’s correlation and had lower error than GVR-O. Accuracy of GEBV for selection candidates was significantly higher when GIBD-L was used and identical between GVR-O and GVR-B. In real data, GIBD-L’s sampling standard deviation was the closest to the theoretical value for each pedigree relationship. Use of pedigree to calculate GR improved the precision of estimates and the accuracy of GEBV.
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Accurate prediction of breeding values depends on capturing the variability in genome sharing of relatives with the same pedigree relationship.
Here, we compare two approaches to set up genomic relationship matrices for precision of genomic relationships (GR) and accuracy of estimated breeding values (GEBV). Real and simulated data (pigs, 60k SNP) were analysed, and GR were estimated using two approaches: (i) identity by state, corrected with either the observed (GVR-O) or the base population (GVR-B) allele frequencies and (ii) identity by descent using linkage analysis (GIBD-L). Estimators were evaluated for precision and empirical bias with respect to true pedigree IBD GR. All three estimators had very low bias. GIBD-L displayed the lowest sampling error and the highest correlation with true genome-shared values. GVR-B approximated GIBD-L’s correlation and had lower error than GVR-O. Accuracy of GEBV for selection candidates was significantly higher when GIBD-L was used and identical between GVR-O and GVR-B. In real data, GIBD-L’s sampling standard deviation was the closest to the theoretical value for each pedigree relationship.
Use of pedigree to calculate GR improved the precision of estimates and the accuracy of GEBV.

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