Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana)

BMC Genomics

Research Article

Jean Bousquet¹  Sébastien Clément²  Jean Beaulieu³  Patrick R.N. Lenz³  Shawn D. Mansfield⁴  Mireille Desponts⁵ 

[1] Canada Research Chair in Forest Genomics, Institute of Systems and Integrative Biology and Centre for Forest Research, Université Laval, 1030, Avenue de la Médecine, G1V 0A6, Québec, Québec, Canada;Canadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Government of Canada, P.O. Box 10380, 1055 du PEPS, G1V 4C7, Québec, Québec, Canada;Canadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Government of Canada, P.O. Box 10380, 1055 du PEPS, G1V 4C7, Québec, Québec, Canada;Canada Research Chair in Forest Genomics, Institute of Systems and Integrative Biology and Centre for Forest Research, Université Laval, 1030, Avenue de la Médecine, G1V 0A6, Québec, Québec, Canada;Department of Wood Science, Forest Sciences Centre, University of British Columbia, V6T 1Z4, Vancouver, British Columbia, Canada;Ministère des Forêts, de la Faune et des Parcs, Gouvernement du Québec, Direction de la recherche forestière, 2700 rue Einstein, G1P 3W8, Québec, Québec, Canada;

关键词: Genomic selection;    Black spruce;    Wood properties;    Tree improvement and breeding;    Genomic-estimated breeding values;    Gene SNPs;   

DOI  :  10.1186/s12864-017-3715-5

 received in 2016-12-13, accepted in 2017-04-21,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGenomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy.ResultsThe study relied on 734 25-year-old trees belonging to 34 full-sib families derived from 27 parents and that were established on two contrasting sites. Genomic profiles were obtained from 4993 Single Nucleotide Polymorphisms (SNPs) representative of as many gene loci distributed among the 12 linkage groups common to spruce. GS models were obtained for four growth and wood traits. Validation using independent sets of trees showed that GS model accuracy was high, related to trait heritability and equivalent to that of conventional pedigree-based models. In forward selection, gains per unit of time were three times higher with the GS approach than with conventional selection. In addition, models were also accurate across sites, indicating little genotype-by-environment interaction in the area investigated. Using information from half-sibs instead of full-sibs led to a significant reduction in model accuracy, indicating that the inclusion of relatedness in the model contributed to its higher accuracies. About 500 to 1000 markers were sufficient to obtain GS model accuracy almost equivalent to that obtained with all markers, whether they were well spread across the genome or from a single linkage group, further confirming the implication of relatedness and potential long-range linkage disequilibrium (LD) in the high accuracy estimates obtained. Only slightly higher model accuracy was obtained when using marker subsets that were identified to carry large effects, indicating a minor role for short-range LD in this population.ConclusionsThis study supports the integration of GS models in advanced-generation tree breeding programs, given that high genomic prediction accuracy was obtained with a relatively small number of markers due to high relatedness and family structure in the population. In boreal spruce breeding programs and similar ones with long breeding cycles, much larger gain per unit of time can be obtained from genomic selection at an early age than by the conventional approach. GS thus appears highly profitable, especially in the context of forward selection in species which are amenable to mass vegetative propagation of selected stock, such as spruces.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

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