Genomic prediction with non-additive effects in beef cattle: Stability of variance component and genetic effect estimates against population size

Background Genomic prediction is now an essential technology for genetic
improvement in animal and plant breeding. Whereas emphasis has been placed
on predicting the breeding values, the prediction of non-additive genetic
effects has also been of interest. Thus, we assessed the potential of
genomic prediction using non-additive effects for phenotypic prediction in
Japanese Black, a beef cattle breed. In addition, we examined the
stability of variance component and genetic effect estimates against
population size by subsampling with different sample sizes. Results
Records of six carcass traits, namely, carcass weight (CW), rib eye area
(REA), rib thickness (RT), subcutaneous fat thickness (SFT), yield rate
(YI) and beef marbling score (BMS), for 9850 animals were used for
analyses. As the non-additive genetic effects, dominance,
additive-by-additive, additive-by-dominance and dominance-by-dominance
effects were considered. The covariance structures of these genetic
effects were defined using genome-wide SNPs. Using single-trait animal
models with different combinations of genetic effects, it was found that
12.6–19.5% of phenotypic variance were occupied by the
additive-by-additive variance, whereas little dominance variance was
observed. In cross-validation, adding the additive-by-additive effects had
little influence on predictive accuracy and bias. Subsampling analyses
showed that estimation of the additive-by-additive effects was highly
variable when phenotypes were not available. On the other hand, the
estimates of the additive-by-additive variance components were less
affected by reduction of the population size. The analysis was 
Dataset Here we deposited a dataset that is necessary to reproduce
variance component estimation and cross-validation analyses, that is,
phenotypic values adjusted with fixed effects
(Pheno.AdbyNonA.csv), and the additive and dominance genomic relationship
matrices (A.csv and D.csv, respectively). These data were taken by a
private company, the Livestock Improvement Association of Japan, Inc.,
under the Japanese animal welfare regulation. The data analysis and data
organization was partially supproted by JSPS KAKENHI Grant Number
18K14567.