Increased Prediction Ability in Norway Spruce Trials Using a Marker X Environment Interaction and Non-Additive Genomic Selection Model

Abstract

A genomic selection study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in 2 Northern Swedish trials at 2 locations using exome capture as a genotyping platform. Nonadditive effects including dominance and first-order epistatic interactions (including additive-by-additive, dominance-by-dominance, and additive-by-dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. Genomic selection models partitioned additive and nonadditive genetic variances more precisely than pedigree-based models. In addition, predictive ability in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to genomic selection per year increased up to 78.9/80.8%, 86.9/82.9%, and 91.3/88.2% compared with response to phenotypic selection per year when genomic selection was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for sites 1 and 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also when they are significant may improve the genetic gain. For tree height, Pilodyn, and modulus of elasticity (MOE), response to genomic selection per year improved up to 68.9%, 91.3%, and 92.6% compared with response to phenotypic selection per year, respectively.Subject Area: Quantitative genetics and Mendelian inheritance

Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

“…In this study, the high dominance ratios observed for the three growth traits (Tables 2 and 4) may explain the high predictive abilities and prediction accuracies of GBLUP_AD models compared to that of the additive models. Similar results were also observed by Chen et al (2019) for tree height in Norway Spruce.…”

“…In forest trees, several studies have used the GBLUP method to test the accuracy of genomic selection (Zapata-Valenzuela et al 2013;Isik et al 2016;Kainer et al 2018;Chen et al 2019). In the GBLUP, the pedigree-based additive relationship matrix is replaced with a realized genomic relationship matrix (GRM) from markers.…”

Advantage of clonal deployment in Norway spruce (Picea abies (L.) H. Karst)

“…In this study, we did not observe any non-additive effects for acoustic velocity (AV). In contrast, Chen et al (2019) found small but appreciable dominance and epistatic effects for AV in two full-sib progeny trials of Norway spruce in northern Sweden. It seems there was less non-additive genetic variance in wood quality traits than in growth traits in Norway spruce as similarly observed in other pines (Wu et al 2008), which may partly be a result of less field environmental error or nursery treatment effects (e.g., sizes of cutting or positions of ortet influence, one of C effects (Burdon and Shelbourne 1974) in forestry) affecting the wood properties compared with growth traits.…”

“…Thus, clonal field trials with family structure are a more common method to estimate non-additive, in particular, epistatic genetic variance. Currently, genomic data with numerous markers distributed genome-wide using exome capture (Thistlethwaite et al 2017;Chen et al 2019), Genotypingby-sequencing (GBS) (Ratcliffe et al 2015) or SNP-chips (Tan et al 2018) are becoming gradually available and genomic relationship matrices for additive, dominance, and epistatic effects can be calculated to estimate the additive, dominance, and epistasis genetic variances. Predicted genomic breeding values can also be compared with the traditional pedigree-based breeding values (Muñoz et al 2014).…”

“…In contrast, another model proposed by Wu (1996) was based on the assumption that epistasis for a quantitative trait is limited to interactions between pairs of quantitative trait loci (QTL) only. Currently, many tree species based on genomic data has discovered significant epistatic genetic variance based on the interaction between QTL pairs, such as additive × additive, additive × dominance, and dominance × dominance interaction effects (Muñoz et al 2014;Bouvet et al 2016;Gamal El-Dien et al 2016;Chen et al 2019;Tan et al 2018). Without clonally replicated experiments, however, it is difficult to explain how total genetic variance is distributed among the three components and make a sound judgment in optimal deployment strategy.…”