Data from: Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

Genomic selection (GS) is of interest in breeding because of its potential
for predicting the genetic value of individuals and increasing genetic
gains per unit of time. To date, very few studies have reported empirical
results of GS potential in the context of large population sizes and long
breeding cycles such as for boreal trees. In this study, we assessed the
effectiveness of marker-aided selection in an undomesticated white spruce
(Picea glauca (Moench) Voss) population of large effective size using a GS
approach. A discovery population of 1694 trees representative of 214
open-pollinated families from 43 natural populations was phenotyped for 12
wood and growth traits and genotyped for 6385 single-nucleotide
polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to
predict estimated breeding values using all the available SNPs or SNP
subsets of the largest absolute effects, and they were validated using
various cross-validation schemes. The accuracy of genomic estimated
breeding values (GEBVs) varied from 0.327 to 0.435 when the training and
the validation data sets shared half-sibs that were on average 90% of the
accuracies achieved through traditionally estimated breeding values. The
trend was also the same for validation across sites. As expected, the
accuracy of GEBVs obtained after cross-validation with individuals of
unknown relatedness was lower with about half of the accuracy achieved
when half-sibs were present. We showed that with the marker densities used
in the current study, predictions with low to moderate accuracy could be
obtained within a large undomesticated population of related individuals,
potentially resulting in larger gains per unit of time with GS than with
the traditional approach.