Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing

Abstract

BackgroundGenomic 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.ResultsGenotyping-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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1597-y) contains supplementary material, which is available to authorized users.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

“…Our results revealed that the prediction accuracy estimated in the candidate population (on average r(Â can , Ŷ can ) = 0.242 and r(Ĝ can , Ŷ can ) = 0.242) remained low compared with the goodness-of-fit (on average r(Â full , Ŷ full ) = 0.513 and r(Ĝ full , Ŷ full ) = 0.649) and prediction stability (on average r(Â can , Â full ) = 0.747 and r(Ĝ can , Ĝ full ) = 0.567) using the full data set. Although comparisons with other studies should be carried out with caution because the prediction accuracies are calculated according to different methods and formulas, the prediction stability values assessed in our study were similar to those estimated in previous forest tree studies for growth traits (Resende et al, 2012;Muñoz et al, 2014;Beaulieu et al, 2014a, b;Gamal El-Dien et al, 2015). The prediction accuracy defined in our study by r(Â can , Ŷ can ) and r(Ĝ can , Ŷ can ) is supposed to give a more objective genomic selection potential.…”

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

“…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]. Our result however differ from previous studies in forest trees due to the fact that the average pairwise estimates of genetic relationship among individuals were substantially lower using SNP data than expectations based on pedigree information (Table 1), clearly suggesting that the expected pedigrees, and consequently the pairwise relationships, had considerable inconsistencies that were corrected by the SNP data.…”

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus

“…Given the relatively long-range LD and relatedness in our populations, our estimates of genomic heritability should closely reflect the amount of additive genetic variance for the traits measured. Genomic heritabilities lower than the pedigree-based estimates were also reported in open-pollinated families of spruce [19, 21]. Pedigree-based heritability estimates from open-pollinated families could be inflated due to the presence of full-sibs or selfs and the inability of these estimates to disentangle the non-additive from the additive genetic components [48].…”