DOI: 10.5061/dryad.tdz08kpz4
title: Genomic prediction with non-additive effects in beef cattle: Stability of variance component and genetic effect estimates against population size

description: 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.
description: 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.