10.5061/DRYAD.PK0P2NGHN
Chen, Zhi-Qiang
0000-0001-9725-8929
Swedish University of Agricultural Sciences
Baison, John
Swedish University of Agricultural Sciences
Pan, Jin
Banat University of Agricultural Sciences and Veterinary Medicine
Westin, Johan
0000-0003-1033-1826
Forestry Research Institute of Sweden
Garcia Gil, Maria Rosario
Swedish University of Agricultural Sciences
Wu, Harry X.
Swedish University of Agricultural Sciences
Accuracy of genomic selection for growth and wood quality traits in two
control-pollinated progeny trials using exome capture as genotyping
platform in Norway spruce
Dryad
dataset
2019
Bayesian LASSO
Bayesian ridge regression
Exome Capture
GBLUP
genotype-by-environment interaction
Norway spruce
RKHS
2019-10-25T00:00:00Z
2019-10-25T00:00:00Z
en
https://doi.org/10.1186/s12864-018-5256-y
39283391 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
A genomic selection (GS) study of growth and wood quality traits is
reported based on control-pollinated Norway spruce families established in
two Northern Swedish trials at two locations using exome capture as
a genotyping platform. Non-additive effects including dominance and
first-order epistatic interactions (including additive-by-additive,
dominance-by-dominance, and additive-by-dominance) and
marker-by-environment interaction (MxE) effects were dissected in genomic
and phenotypic selection models. GS models partitioned additive and
non-additive genetic variances more precisely than pedigree-based models.
In addition, predictive ability (PA) in GS was substantially increased by
including dominance and slightly increased by including M´E effects when
these effects are significant. For velocity, response to GS (RGS) per year
increased 78.9/80.8%, 86.9/82.9%, and 91.3/88.2% compared with response to
phenotypic selection (RPS) per year when GS was based on 1) main marker
effects (M), 2) M + M´E effects (A), and 3) A + dominance effects (AD)
for sites 1 and 2, respectively. This indicates that including MxE and
dominance effects not only improves genetic parameter estimates but
also when they are significant may improve the genetic gain. For tree
height, Pilodyn, and modulus of elasticity (MOE), RGS per year improved up
to 68.9%, 91.3%, and 92.6% compared with RPS per year, respectively.