Data from: Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

Hybrids are broadly used in plant breeding and accurate estimation of
variance components is crucial for optimizing genetic gain. Genome-wide
information may be used to explore models designed to assess the extent of
additive and non-additive variance and test their prediction accuracy for
the genomic selection. Ten linear mixed models, involving pedigree- and
marker-based relationship matrices among parents, were developed to
estimate additive (A), dominance (D) and epistatic (AA, AD and DD)
effects. Five complementary models, involving the gametic phase to
estimate marker-based relationships among hybrid progenies, were developed
to assess the same effects. The models were compared using tree height and
3303 single-nucleotide polymorphism markers from 1130 cloned individuals
obtained via controlled crosses of 13 Eucalyptus urophylla females with 9
Eucalyptus grandis males. Akaike information criterion (AIC), variance
ratios, asymptotic correlation matrices of estimates, goodness-of-fit,
prediction accuracy and mean square error (MSE) were used for the
comparisons. The variance components and variance ratios differed
according to the model. Models with a parent marker-based relationship
matrix performed better than those that were pedigree-based, that is, an
absence of singularities, lower AIC, higher goodness-of-fit and accuracy
and smaller MSE. However, AD and DD variances were estimated with high
s.es. Using the same criteria, progeny gametic phase-based models
performed better in fitting the observations and predicting genetic
values. However, DD variance could not be separated from the dominance
variance and null estimates were obtained for AA and AD effects. This
study highlighted the advantages of progeny models using genome-wide
information.