Data from: A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods

Genomic selection (GS) potentially offers an unparalleled advantage over
traditional pedigree-based selection (TS) methods by reducing the time
commitment required to carry out a single cycle of tree improvement. This
quality is particularly appealing to tree breeders, where lengthy
improvement cycles are the norm. We explored the prospect of implementing
GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped
population of 769 trees belonging to 25 open-pollinated families. A series
of repeated tree height measurements through ages 3–40 years permitted the
testing of GS methods temporally. The genotyping-by-sequencing (GBS)
platform was used for single nucleotide polymorphism (SNP) discovery in
conjunction with three unordered imputation methods applied to a data set
with 60% missing information. Further, three diverse GS models were
evaluated based on predictive accuracy (PA), and their marker effects.
Moderate levels of PA (0.31–0.55) were observed and were of sufficient
capacity to deliver improved selection response over TS. Additionally, PA
varied substantially through time accordingly with spatial competition
among trees. As expected, temporal PA was well correlated with age-age
genetic correlation (r=0.99), and decreased substantially with increasing
difference in age between the training and validation populations
(0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest
neighbor and singular value decomposition yielded a greater number of SNPs
and gave higher predictive accuracies than imputing with the mean.
Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both
yielded equal, and better PA than the generalized ridge regression
heteroscedastic effect model for the traits evaluated.