Genomic selection accuracies within and between environments and small breeding groups in white spruce

BMC Genomics

Jean Bousquet¹  André Rainville²  John MacKay¹  Trevor K Doerksen³  Jean Beaulieu¹ 

[1] Canada Research Chair in Forest and Environmental Genomics, Institute for Systems and Integrative Biology, Université Laval, Quebec City, QC G1V 0A6, Canada;Ministère des Forêts, de la Faune et des Parcs du Québec, Direction de la recherche forestière, 2700 rue Einstein, Quebec City, QC G1P 3 W8, Canada;Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, 1055 du P.E.P.S, Stn. Sainte-Foy, P.O. Box 10380, Quebec City, QC G1V 4C7, Canada

关键词: Spruce;    Somatic embryogenesis;    Relatedness;    Genotype-by-environment interactions;    Genomic selection;   

Others  :  1090119
DOI  :  10.1186/1471-2164-15-1048

 received in 2014-06-12, accepted in 2014-11-19,  发布年份 2014

【 摘 要 】

Background

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of N_e ≈ 20. Marker subsets were also tested.

Results

Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71–0.79) and moderately high for growth (r = 0.52–0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.

Conclusions

Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

【 授权许可】

   
2014 Beaulieu et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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