Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Abstract

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.

Non-additive Effects in Genomic Selection

“…Genomic selection models with dominance have been tested in several populations, including dairy cattle (Ertl et al, 2014 ; Aliloo et al, 2016 ; Jiang et al, 2017 ), pigs (Esfandyari et al, 2016 ; Xiang et al, 2016 ), sheep (Moghaddar and van der Werf, 2017 ), and layers (Heidaritabar et al, 2016 ) with ambiguous results. Jiang et al ( 2017 ) found a negligible percentage of variation explained by dominance effects for productive life in a Holstein cattle population, although Ertl et al ( 2014 ) suggested that dominance may suppose up to 39% of the total genetic variation for Somatic Cell Score in a population of Fleckvieh cattle.…”

Genomic selection models for directional dominance: an example for litter size in pigs

“…The Gibbs sampler also allows to calculate posterior distributions of the any combination of parameters of the model. Thus, we calculated for each iteration of the Gibbs sampler the following approximations for the additive and dominance genetic variances [ 47 ] as: where is the number of individuals in the population and and the breeding and the dominance deviation values for the - th individual. They were calculated as: where as defined by Xiang et al [ 13 ], was the observed allelic frequency for the - th SNP and Note that this parameterization implies a population under Hardy–Weinberg equilibrium and that the covariance between the breeding values and dominance deviations is equal to zero.…”

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations

“…However, Ertl et al (2014) reported a range of low to very high ratio of dominance to total genetic variance for 9 milk production and conformation traits in Fleckvieh cattle and between 3.3% and 50.5% of the total genetic variance. Heidaritabar et al (2016) reported the ratio of dominance to phenotypic variance in egg production traits in purebred layer hens from 3.0% to 22% based on pedigree BLUP, from 0.0% to 3.0% based on GBLUP and from 1.0% to 5.0% based on a Bayesian approach (BayesC).…”