Apr 30, 2017

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
Patrick R N LenzJean Bousquet

Abstract

Genomic 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. The 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...Continue Reading

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References

Mentioned in this Paper

Biological Markers
Study
Genome
Trees (plant)
Dysequilibrium Syndrome
Picea
Genetic Markers
Genetic Pedigree
Long QT Syndrome
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