|Title||Genomic prediction of growth in pigs based on a model including additive and dominance effects|
|Author(s)||Lopes, M.S.; Bastiaansen, J.W.M.; Janss, L.; Knol, E.F.; Bovenhuis, H.|
|Source||Journal of Animal Breeding and Genetics (2016). - ISSN 0931-2668 - p. 180 - 186.|
Animal Breeding and Genetics
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Phenotype prediction - SNP - Variance component|
Independent of whether prediction is based on pedigree or genomic information, the focus of animal breeders has been on additive genetic effects or 'breeding values'. However, when predicting phenotypes rather than breeding values of an animal, models that account for both additive and dominance effects might be more accurate. Our aim with this study was to compare the accuracy of predicting phenotypes using a model that accounts for only additive effects (MA) and a model that accounts for both additive and dominance effects simultaneously (MAD). Lifetime daily gain (DG) was evaluated in three pig populations (1424 Pietrain, 2023 Landrace, and 2157 Large White). Animals were genotyped using the Illumina SNP60K Beadchip and assigned to either a training data set to estimate the genetic parameters and SNP effects, or to a validation data set to assess the prediction accuracy. Models MA and MAD applied random regression on SNP genotypes and were implemented in the program Bayz. The additive heritability of DG across the three populations and the two models was very similar at approximately 0.26. The proportion of phenotypic variance explained by dominance effects ranged from 0.04 (Large White) to 0.11 (Pietrain), indicating that importance of dominance might be breed-specific. Prediction accuracies were higher when predicting phenotypes using total genetic values (sum of breeding values and dominance deviations) from the MAD model compared to using breeding values from both MA and MAD models. The highest increase in accuracy (from 0.195 to 0.222) was observed in the Pietrain, and the lowest in Large White (from 0.354 to 0.359). Predicting phenotypes using total genetic values instead of breeding values in purebred data improved prediction accuracy and reduced the bias of genomic predictions. Additional benefit of the method is expected when applied to predict crossbred phenotypes, where dominance levels are expected to be higher.