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

Abstract

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. In this study, 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, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, 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. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

Heritability and genetic correlation estimates of semen production traits with litter traits and pork production traits in purebred Duroc pigs

“…Several studies have estimated a lower permanent environmental variance when adding non-additive genetic effects, such as dominance and epistatic effects, into repeatability model ( Nagy et al, 2013 ; Aliloo et al, 2016 ; Aliloo et al, 2017 ; Vitezica et al, 2018 ). However, the possibility of more accurate breeding value prediction by adding these effects ( Varona et al, 2018 ) may be limited, because introducing those effects brings additional covariances among individuals, as well as the difficulty in accurate estimation of non-additive genetic effects and the possible confounding problem observed in various cases, including ones using genome-wide SNP markers ( Hill et al, 2008 ; Lee et al, 2010 ; Vitezica et al, 2013 ; Nagy et al, 2014 ; Bolormaa et al, 2015 ; Aliloo et al, 2016 ; Aliloo et al, 2017 ; Raidan et al, 2018 ; Joshi et al, 2020 ; Onogi et al, 2021 ).…”

Genomic heritability and correlation between carcass traits in Japanese Black cattle evaluated under different ceilings of relatedness among individuals

“…The phenotypic values associated with carcass traits are presented entirely by Onogi et al (2021) . As shown in Table 1 , the average values for CW, RE, RT, BMS, and YI were larger than those reported by previous studies in Japanese Black cattle ( Mukai et al, 1995 ; Sasaki et al, 2006 ; Zoda et al, 2022 ).…”

“…The data were collected in the progeny testing program of the Japanese Black cattle by the Livestock Improvement Association of Japan, Inc. (LIAJ). The set of data analyzed here is fully explained by Onogi et al (2021) . Briefly, a total of 9,850 animals (4,142 heifers and 5,708 steers) from 487 sires were used in this study.…”

“…The genetic relationship matrix calculates and scores a value for each pair of individuals in a data set using the estimates of genomic sharing and comparing two genomes. GRM was provided by Onogi et al (2021) ( https://doi.org/10.5061/dryad.tdz08kpz4 ). We prepared GRM binary file using R v4.1.3 software for the subsequent analysis ( Team, 2013 ).…”

Performance of using opposing homozygotes for paternity testing in Japanese Black cattle

“…Recently, genomic prediction for traits other than carcass traits (e.g. Atagi et al, 2017;Onogi et al, 2015;Takeda et al, 2020) and that with non-additive genetic effects for carcass traits (Onogi et al, 2021) have been also studied. Enlarging the training population size for accurate genetic prediction (e.g.…”