Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce

Simon Nadeau; Jean Beaulieu; Salvador A Gezan; Martin Perron; Jean Bousquet; Patrick R N Lenz

doi:10.3389/fpls.2023.1137834

Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce

Front Plant Sci. 2023 Mar 22:14:1137834. doi: 10.3389/fpls.2023.1137834. eCollection 2023.

Authors

Simon Nadeau¹, Jean Beaulieu², Salvador A Gezan³, Martin Perron^{2

4}, Jean Bousquet², Patrick R N Lenz^{1

2}

Affiliations

¹ Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Québec, QC, Canada.
² Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC, Canada.
³ VSN International Ltd, Hemel Hempstead, United Kingdom.
⁴ Direction de la Recherche Forestière, Ministère des Ressources Naturelles et des Forêts, Québec, QC, Canada.

Abstract

Introduction: Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains.

Methods: Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models.

Results: While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely.

Discussion: This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material.

Keywords: GBLUP; Genomic selection (GS); conifers; growth traits; mate allocation; non-additive genetic effects; tree breeding programs; wood quality traits.

Grants and funding

This study was made possible through funding from the Natural Resources Canada Genomics R&D Initiative and the Canadian Wood Fibre Centre to JBe and PRNL, and through support to JBo from the Canada Research Chair in Forest Genomics, and to JBo and PRNL from the Spruce-Up project (234 FOR) co-lead by JBo and J. Bohlmann, and funded by the Large-Scale Applied Research Program (LSARP) of Genome Canada, Génome Québec and Genome British Columbia. This work is also part of the white spruce breeding program project number 142332077 conducted by MP at the Direction de la recherche forestière (Ministère des Ressources naturelles et des Forêts, Quebec, Canada).