TY - JOUR
T1 - Benefits of Dominance over Additive Models for the Estimation of Average Effects in the Presence of Dominance
JF - G3: Genes|Genomes|Genetics
SP - 3405
LP - 3414
DO - 10.1534/g3.117.300113
VL - 7
IS - 10
AU - Duenk, Pascal
AU - Calus, Mario P. L.
AU - Wientjes, Yvonne C. J.
AU - Bijma, Piter
Y1 - 2017/10/01
UR - http://www.g3journal.org/content/7/10/3405.abstract
N2 - 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 () 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 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 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.
ER -