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 genotypingby-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.

Evaluating the accuracy of genomic prediction of growth and wood traits in twoEucalyptusspecies and their F1hybrids

“…In loblolly pine, for example, the performance of rrBLUP and three Bayesian methods were only marginally different when compared across 17 traits with distinct heritabilities, with a small improvement seen for BayesA only for fusiform rust resistance where loci of relatively larger effect have been described [44]. Similar results were obtained for growth and wood traits in other forest trees showing no performance difference between rrBLUP and Bayesian methods [46,48,49]. This occurs despite simulation studies suggesting that Bayesian methods, like BL, should outperform univariate methods such as rrBLUP and GBLUP [6,52,53].…”

“…3). While similar results have been reported for animals [18,43] and crop species [9,36] across a number of traits, in forest trees prediction accuracies using genomic data have generally been similar or up to 10-30% lower than accuracies obtained using pedigree-estimated breeding values, including Eucalyptus [4], loblolly pine (Pinus taeda) [44], white spruce (Picea glauca) [45,46], interior spruce (Picea engelmannii × glauca) [47,48] and maritime pine (Pinus pinaster) [49]. Genomic predictions with lower accuracies than pedigree-based predictions could arise from insufficient marker density, such that not all casual variants are captured in the genomic estimate [41], or an overestimate of the pedigree-based prediction due to its inability of ascertaining the true genetic relationships in half-sib families [47].…”

Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana)

“…However, models built with half-sibs had higher accuracies than those from previous reports similarly derived from half-sib families of eastern white spruce or western hybrid spruce in Canada [26, 28]. The difference is likely due to the low effective population size of half-sibs in the present study, leading to a higher level of relatedness among trees in the training and validation sets compared to true open-pollinated families where a large number of mostly unrelated pollen donors intervene and greatly increase the effective population size [26].…”

Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application

“…Genomic selection is being increasingly explored as a major tool for selection in forest tree breeding Isik et al 2011Isik et al , 2016Lexer and Stölting 2012;Ratcliffe et al 2015;Resende et al 2011Resende et al , 2012a. The major benefit of genomic selection is that selection can be undertaken well before the normal age of phenotypingaround age 8 in radiata pine.…”