Benefits of Dominance over Additive Models for the Estimation of Average Effects in the Presence of Dominance

Abstract

In quantitative genetics, the average effect at a single locus can be estimated by an additive (A) model, or an additive plus dominance (AD) model. In the presence of dominance, the AD-model is expected to be more accurate, because the A-model falsely assumes that residuals are independent and identically distributed. Our objective was to investigate the accuracy of an estimated average effect (αtrue^) in the presence of dominance, using either a single locus A-model or AD-model. Estimation was based on a finite sample from a large population in Hardy-Weinberg equilibrium (HWE), and the root mean squared error of αtrue^ was calculated for several broad-sense heritabilities, sample sizes, and sizes of the dominance effect. Results show that with the A-model, both sampling deviations of genotype frequencies from HWE frequencies and sampling deviations of allele frequencies contributed to the error. With the AD-model, only sampling deviations of allele frequencies contributed to the error, provided that all three genotype classes were sampled. In the presence of dominance, the root mean squared error of αtrue^ with the AD-model was always smaller than with the A-model, even when the heritability was less than one. Remarkably, in the absence of dominance, there was no disadvantage of fitting dominance. In conclusion, the AD-model yields more accurate estimates of average effects from a finite sample, because it is more robust against sampling deviations from HWE frequencies than the A-model. Genetic models that include dominance, therefore, yield higher accuracies of estimated average effects than purely additive models when dominance is present.

Optimum mating designs for exploiting dominance in genomic selection schemes for aquaculture species

“…Although several authors have proposed to include dominance in genomic evaluation models to obtain more accurate estimation of breeding values [ 7 – 13 ] or to optimize the mating scheme [ 6 , 14 ], non-additive genetics effects are usually ignored in animal breeding programs. As mentioned previously, one of the reasons for this is that low accuracies are obtained when dealing with pedigrees that do not allow precise estimation of dominance effects, even when genomic information is available [ 17 ]. However, the structure of aquaculture breeding populations, with large full-sib families, facilitates estimation of dominance effects.…”

“…The availability of genomic information may make the estimation of dominance effects easier, and several authors have included dominance effects in genomic evaluation models [ 6 – 16 ]. However, even when using genomic data, the accuracy of estimates of dominance effects still depends on the number of individuals sampled and the structure of the population (in particular, the presence or not of large full-sib families) [ 17 ].…”

“…As suggested in the literature, estimation of breeding values and dominance deviations to predict the expected offspring genotype required for MS could benefit from the use of the high-density single nucleotide polymorphism (SNP) genotyping tools that are currently available [ 6 , 17 ]. With sufficient markers, the genomic relationship matrices for both additive and dominance effects are expected to be more accurate than pedigree-based matrices.…”

Non-additive Effects in Genomic Selection

“…In any case, only additive values (substitution effects) contribute to breeding values and are therefore expressed in the next generation. However, estimates of non-additive genetic effects may be of relevance because: (i) they may contribute to increasing the accuracy of prediction of breeding values and the response to selection (Toro and Varona, 2010 ; Aliloo et al, 2016 ; Duenk et al, 2017 ); (ii) they allow the definition of mate allocation procedures between candidates for selection (Maki-Tanila, 2007 ; Toro and Varona, 2010 ; Aliloo et al, 2017 ); and (iii) they can be used to benefit from non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes (Maki-Tanila, 2007 ; Zeng et al, 2013 ).…”

Interdigital Hyperplasia in Holstein Cattle Is Associated With a Missense Mutation in the Signal Peptide Region of the Tyrosine-Protein Kinase Transmembrane Receptor Gene

“…After filtering 70 samples and 45,232 SNPs remained in the analysis. Genome-wide associations were calculated under an additive and dominant model (Duenk et al, 2017). Associations were regarded as genome-wide statistically significant above a threshold of -log 10 P = 5.47 ( p = 0.05) (Benjamini and Yekutieli, 2001).…”