Genomic studies with preselected markers reveal dominance effects influencing growth traits in Eucalyptus nitens

Abstract

Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait-relevant markers may be important compared to nonselected markers. Here, we used preselected markers to study the dominance effects in a Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN), and kraft pulp yield (KPY) were analyzed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested (parents, genotyped, and nongenotyped). Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is nonlinear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce

“…Recently, several studies on tree species have reported that selecting markers that have a particular influence over the studied trait could improve the predictive ability (Thumma et al , 2022). Tan and Ingvarsson (2022) (Tan and Ingvarsson, 2022) reported that a careful 1% preselection of markers could improve the estimate of heritability and genomic prediction in a Eucalyptus population.…”

“…latifolia , Cappa et al (2022) reported that selecting informative markers, in particular markers capturing ancestry/population structure, can improve the predictive ability. In Eucalyptus nitens , Thumma et al (2022) developed a marker panel based on selected candidates and genotyped with a “targeted genotyping-by-sequencing (TGD)” method and revealed dominance effects influencing growth.…”

“…Several studies in trees have shown that a few thousand randomly selected SNPs may capture most of the variation, and similar GP efficiency as using all available markers (Chen et al , 2018; Estopa et al , 2022; Tan et al , 2017; Thumma et al , 2022). It has also been reported that marker preselection could slightly to moderately improve genomic prediction accuracy in tree species (Cappa et al , 2022; Resende et al , 2012; Tan and Ingvarsson, 2022).…”

“…Several studies in trees have shown that a few thousand randomly selected SNPs may capture most of the variation, and similar GP efficiency as using all available markers [13–16]. It has also been reported that marker preselection could slightly to moderately improve GP accuracy in tree species [17–19].…”

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce

“…Several studies in trees have shown, that GP using a few thousand randomly selected SNPs may capture most of the variation and achieve a similar GP accuracy as using all available markers [ 13 – 16 ]. It has also been reported that marker preselection could slightly-to-moderately improve GP accuracy in tree species [ 17 – 19 ].…”