Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Heidaritabar, M., Wolc, A., Arango, J., Zeng, J., Settar, P., Fulton, J. E., O'Sullivan, N. P., Bastiaansen, J. W. M., Fernando, R. L., Garrick, D. J. and Dekkers, J. C. M. (2016) Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers. Journal of Animal Breeding and Genetics, 133 5: 334-346. doi:10.1111/jbg.12225


Author Heidaritabar, M.
Wolc, A.
Arango, J.
Zeng, J.
Settar, P.
Fulton, J. E.
O'Sullivan, N. P.
Bastiaansen, J. W. M.
Fernando, R. L.
Garrick, D. J.
Dekkers, J. C. M.
Title Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers
Journal name Journal of Animal Breeding and Genetics   Check publisher's open access policy
ISSN 1439-0388
0931-2668
Publication date 2016-10-01
Sub-type Article (original research)
DOI 10.1111/jbg.12225
Open Access Status Not yet assessed
Volume 133
Issue 5
Start page 334
End page 346
Total pages 13
Place of publication Berlin, Germany
Publisher Wiley-Blackwell Verlag GmbH
Collection year 2017
Language eng
Abstract Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01–0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.
Keyword Additive effect
Dominance effect
Genomic selection
Layer
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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