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

Zhi-Qiang Chen,Harry X Wu,John Baison,Johan Westin,Jin Pan,Maria Rosario García Gil,John R Stommel

doi:10.1093/jhered/esz061

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

Highlights

Genomic selection (GS) is a breeding method that uses a dense set of genetic markers to accurately predict the genetic merit of individuals (Meuwissen et al 2001) and it has been incorporated into animal breeding for many years (Van Eenennaam et al 2014)
GS studies have been implemented in several breeding programs, but these studies mostly focused on additive effects in several commercially important conifer species, such as loblolly pine (Pinus taeda L.), Maritime pine (Pinus pinaster Ait.), Norway spruce (Picea abies (L.) Karst.), white spruce (Picea glauca (Moench) Voss) and hardwood Eucalypt species
The log-likelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for the five models (ABLUP-A, ABLUP-AD, GBLUP-A, GBLUP-AD, and GBLUP-ADE) under various variance structures are shown in Table S1

Summary

Introduction

Genomic selection (GS) is a breeding method that uses a dense set of genetic markers to accurately predict the genetic merit of individuals (Meuwissen et al 2001) and it has been incorporated into animal breeding for many years (Van Eenennaam et al 2014). The non-additive contributions have been estimated in several studies (Bouvet et al.2016; de Almeida Filho et al 2016; Gamal El-Dien et al 2016; Muñoz et al 2014; Tan et al 2018). Based on several recent studies, dominance and epistasis may be confounded with the additive effects in both pedigree-based relationship matrix models (Gamal El-Dien et al 2018) and genomic-based relationship matrix models (Tan et al 2018). In the conventional pedigree-based genetic analysis, estimates of different genetic components such as additive, dominance and epistatic variances need full-sib family structure or full-sib family structure plus clonally replicated test (Mullin and Park, 1992). Only a few reliable estimates for the non-additive variation were reported based on pedigree-based relationship (Baltunis et al 2007; Isik et al.2005; Isik et al 2003; Weng et al 2008; Wu et al 2008), especially for wood quality traits (Wu, 2018)

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