10.5061/DRYAD.8KB37
Gamal El-Dien, Omnia
University of British Columbia
Ratcliffe, Blaise
University of British Columbia
Klápste, Jaroslav
University of British Columbia
Chen, Charles
Oklahoma State University
Porth, Ilga
University of British Columbia
El-Kassaby, Yousry A.
University of British Columbia
Data from: Prediction accuracies for growth and wood attributes of
interior spruce in space using genotyping-by-sequencing
Dryad
dataset
2015
Genotyping-by-Sequenicing
Picea glauca (Moench) Voss
Imputation
Picea engelmannii Parry ex Engelm.
Genomic selection
2015-05-15T01:24:33Z
2015-05-15T01:24:33Z
en
https://doi.org/10.1186/s12864-015-1597-y
117394858 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Background: Genomic selection (GS) in forestry can substantially reduce
the length of breeding cycle and increase gain per unit time through early
selection and greater selection intensity, particularly for traits of low
heritability and late expression. Affordable next-generation sequencing
technologies made it possible to genotype large numbers of trees at a
reasonable cost. Results: Genotyping-by-sequencing was used to genotype
1,126 Interior spruce trees representing 25 open-pollinated families
planted over three sites in British Columbia, Canada. Four imputation
algorithms were compared (mean value (MI), singular value decomposition
(SVD), expectation maximization (EM), and a newly derived, family-based
k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and
wood attributes. Single- and multi-site GS prediction models were
developed using the Ridge Regression Best Linear Unbiased Predictor
(RR-BLUP) and the Generalized Ridge Regression (GRR) to test different
assumption about trait architecture. Finally, using PCA, multi-trait GS
prediction models were developed. The EM and kNN-Fam imputation methods
were superior for 30 and 60% missing data, respectively. The RR-BLUP GS
prediction model produced better accuracies than the GRR indicating that
the genetic architecture for these traits is complex. GS prediction
accuracies for multi-site were high and better than those of single-sites
while multi-site predictability produced the lowest accuracies reflecting
type-b genetic correlations and deemed unreliable. The incorporation of
genomic information in quantitative genetics analyses produced more
realistic heritability estimates as half-sib pedigree tended to inflate
the additive genetic variance and subsequently both heritability and gain
estimates. Principle component scores as representatives of multi-trait GS
prediction models produced surprising results where negatively correlated
traits could be concurrently selected for using PCA2 and PCA3.
Conclusions: The application of GS to open-pollinated family testing, the
simplest form of tree improvement evaluation methods, was proven to be
effective. Prediction accuracies obtained for all traits greatly support
the integration of GS in tree breeding. While the within-site GS
prediction accuracies were high, the results clearly indicate that
single-site GS models ability to predict other sites are unreliable
supporting the utilization of multi-site approach. Principle component
scores provided an opportunity for the concurrent selection of traits with
different phenotypic optima.
PhenotypesTree ID (Site, Family, Tree), height, diameter, volume,
velocity, Resistograph, Xray (wood density), MoE (modulus of
elasticity)SNP MI 30%Genotyping-by-sequence imputated SNP file with 30%
missing data using the mean imputation methodSNP MI
60%Genotyping-by-sequence imputated SNP file with 60% missing data using
the mean imputation method
Canada
British Columbia