Data from: The contribution of dominance to phenotype prediction in a pine breeding and simulated population

Pedigrees and dense marker panels have been used to predict the genetic
merit of individuals in plant and animal breeding, accounting primarily
for the contribution of additive effects. However, nonadditive effects may
also affect trait variation in many breeding systems, particularly when
specific combining ability is explored. Here we used models with different
priors, and including additive-only and additive plus dominance effects,
to predict polygenic (height) and oligogenic (fusiform rust resistance)
traits in a structured breeding population of loblolly pine (Pinus taeda
L.). Models were largely similar in predictive ability, and the inclusion
of dominance only improved modestly the predictions for tree height. Next,
we simulated a genetically similar population to assess the ability of
predicting polygenic and oligogenic traits controlled by different levels
of dominance. The simulation showed an overall decrease in the accuracy of
total genomic predictions as dominance increases, regardless of the method
used for prediction. Thus, dominance effects may not be accounted for as
effectively in prediction models compared with traits controlled by
additive alleles only. When the ratio of dominance to total phenotypic
variance reached 0.2, the additive-dominance prediction models were
significantly better than the additive-only models. However, in the
prediction of the subsequent progeny population, this accuracy increase
was only observed for the oligogenic trait.