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.

A.csv and D.csv are the additive and dominance genomic relationship matrices (A and D), respectively.

Pheno.AdbyNonA.csv are the phenotypic values adjusted with fixed effects. The fixed effects were estimated using models with non-additive effects that were selected for prediction (i.e., modells represented in Table 2 in the main text). The column names indicate traits: CW, carcass weight; REA, rib eye area; RT, rib thickness; SFT, subcutaneous fat thickness; YI, yield rate; BMS, beef marbling score.

The numers (9850) and the orders of animals are common among all files.

The additive-by-additive and additive-by-dominance relationship matrices can be generated by multiplying the corresponding matrices (A and D). 

These files will be sufficient to reproduce the results of Tables 2 and 4, and Figures 1, 3, and 4.