title: A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods.
abstract: 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.
authors.affiliation: Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: B
authors.identifier: http://orcid.org/0000-0003-4469-2929
authors.initials: B
authors.lastname: Ratcliffe
authors.affiliation: Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: O G
authors.initials: OG
authors.lastname: El-Dien
authors.affiliation: Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia, Canada.|Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6, Czech Republic.
authors.forename: J
authors.initials: J
authors.lastname: Klápště
authors.affiliation: Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: I
authors.initials: I
authors.lastname: Porth
authors.affiliation: Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, USA.
authors.forename: C
authors.initials: C
authors.lastname: Chen
authors.affiliation: British Columbia Ministry of Forests, Lands and Natural Resource Operations, Tree Improvement Branch, Kalamalka Research Station and Seed Orchard, Vernon, British Columbia, Canada.
authors.forename: B
authors.initials: B
authors.lastname: Jaquish
authors.affiliation: Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Y A
authors.initials: YA
authors.lastname: El-Kassaby
mesh_terms: D001955:British Columbia; D005828:Genetics, Population; D005838:Genotype; D060005:Genotyping Techniques / Q000379:methods; D008957:Models, Genetic; D028222:Picea / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D012641:Selection, Genetic