Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce

Patrick R N Lenz,Nathalie Isabel,Sébastien Gérardi,Simon Nadeau,Marie Deslauriers,Jean Beaulieu,Jean Bousquet,Aïda Azaiez,Martin Perron

doi:10.1038/s41437-019-0290-3

Abstract

Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring’s breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22–52% more accurate than predictions based on the maternal pedigree, and 5–7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61–0.70) and full-sib progeny tests (0.61–0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.

Highlights

Genomic selection (GS), called genomic prediction, relies on genome-wide dense marker maps to model the entire complement of QTL effects across the genome (Meuwissen et al 2001), and relies on relatedness among the individuals making up the training and the target populations (Zhong et al 2009; Zapata-Valenzuela et al 2012; Lenz et al 2017)
One particular maternal family (2254) was more severely affected by pollen contamination as 12 out of 23 offspring were sired by six foreign pollen donors, while for all other contaminated maternal families, one to three offspring were sired by a single foreign pollen donor in each family
We evaluated GS modelling in a polycross mating design and compared the findings with results obtained from an independent full-sib mating design based on the same parents

Summary

Introduction

Genomic selection (GS), called genomic prediction, relies on genome-wide dense marker maps to model the entire complement of QTL effects across the genome (Meuwissen et al 2001), and relies on relatedness among the individuals making up the training and the target populations (Zhong et al 2009; Zapata-Valenzuela et al 2012; Lenz et al 2017). The estimation of an individual genetic merit through its genomic-estimated breeding values (GEBVs) has been demonstrated to be a valuable tool for a wide variety of traits in several forest trees such as Eucalyptus (Resende et al 2012b), pines (Resende et al 2012a; Zapata-Valenzuela et al 2012; Isik et al 2016), and spruces (Beaulieu et al 2014; Chen et al 2018; Lenz et al 2020). That a number of proof-of concept studies highlighted the large potential of GS for hastened and accurate breeding of forest trees, questions remain about how to most effectively deploy GS strategies, for example, which experimental or breeding design is best to use in combination with GS or should conventional breeding strategies be revisited in the light of GS?

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