Data from: Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model

We report a genomic selection (GS) study of growth and wood quality traits
in an outbred F2 hybrid Eucalyptus population (n=768) using high-density
single-nucleotide polymorphism (SNP) genotyping. Going beyond previous
reports in forest trees, models were developed for different selection
targets, namely, families, individuals within families and individuals
across the entire population using a genomic model including dominance. To
provide a more breeder-intelligible assessment of the performance of GS we
calculated the expected response as the percentage gain over the
population average expected genetic value (EGV) for different proportions
of genomically selected individuals, using a rigorous cross-validation
(CV) scheme that removed relatedness between training and validation sets.
Predictive abilities (PAs) were 0.40–0.57 for individual selection and
0.56–0.75 for family selection. PAs under an additive+dominance model
improved predictions by 5 to 14% for growth depending on the selection
target, but no improvement was seen for wood traits. The good performance
of GS with no relatedness in CV suggested that our average SNP density
(~25 kb) captured some short-range linkage disequilibrium. Truncation GS
successfully selected individuals with an average EGV significantly higher
than the population average. Response to GS on a per year basis was ~100%
more efficient than by phenotypic selection and more so with higher
selection intensities. These results contribute further experimental data
supporting the positive prospects of GS in forest trees. Because
generation times are long, traits are complex and costs of DNA genotyping
are plummeting, genomic prediction has good perspectives of adoption in
tree breeding practice.