Data from: Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories

Genomic selection is expected to enhance the genetic improvement of forest
tree species by providing more accurate estimates of breeding values
through marker-based relationship matrices compared with pedigree-based
methodologies. When adequately robust genomic prediction models are
available, an additional increase in genetic gains can be made possible
with the shortening of the breeding cycle through elimination of the
progeny testing phase and early selection of parental candidates. The
potential of genomic selection was investigated in an advanced Eucalyptus
nitens breeding population focused on improvement for solid wood
production. A high-density SNP chip (EUChip60K) was used to genotype 691
individuals in the breeding population, which represented two seed
orchards with different selection histories. Phenotypic records for growth
and form traits at age six, and for wood quality traits at age seven were
available to build genomic prediction models using GBLUP which were
compared to the traditional pedigree-based alternative using BLUP. GBLUP
demonstrated that breeding value accuracy would be improved and
substantial increases in genetic gains towards solid wood production would
be achieved. Cross-validation within and across two different seed
orchards indicated that genomic predictions would likely benefit in terms
of higher predictive accuracy from increasing the size of the training
data sets through higher relatedness and better utilization of LD