Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality.

Autor: Bermann M; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA., Legarra A; UMR GenPhySE, INRA Toulouse, Castanet Tolosan, France., Hollifield MK; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA., Masuda Y; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA., Lourenco D; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA., Misztal I; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA.
Jazyk: angličtina
Zdroj: Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie [J Anim Breed Genet] 2021 Jan; Vol. 138 (1), pp. 4-13. Date of Electronic Publication: 2020 Sep 28.
DOI: 10.1111/jbg.12507
Abstrakt: The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated data sets with variances from the reference and validation populations. The method was tested using simulated and real chicken data sets. The simulated data set included 10 generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real data set included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both data sets were analysed using best linear unbiased predictor (BLUP) or single-step GBLUP (ssGBLUP). The whole data set included all phenotypes available, whereas in the partial data set, phenotypes of the most recent generation were removed. In the simulated data set, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs (i.e. true accuracies) were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was <0.02. Accuracies of BLUP and ssGBLUP with the real data set were 0.41 and 0.47, respectively. This small improvement in accuracy when using ssGBLUP with the real data set was due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods as k-fold validation and predictive ability are not applicable.
(© 2020 The Authors. Journal of Animal Breeding and Genetics published by Wiley-VCH GmbH.)
Databáze: MEDLINE
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