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

• Main Authors: Pascal Duenk, Mario P. L. Calus, Yvonne C. J. Wientjes, Piter Bijma
• Format: Article
• Language: English
• Published: Oxford University Press 2017-10-01
• Series: G3: Genes, Genomes, Genetics
• Subjects: dominance; Hardy-Weinberg equilibrium; average effect; root mean squared error; accuracy
• Online Access: http://g3journal.org/lookup/doi/10.1534/g3.117.300113

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 (α^ ) 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 α^ 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 α^ 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.