BACKGROUND: Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. RESULTS: Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. CONCLUSIONS: Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.

Beijing Advanced Innovation Centre for Tree Breeding by Molecular Design Beijing Forestry University Beijing 100083 China

Department of Forestry and Environmental Resources North Carolina State University Raleigh NC 27695 USA

Department of Genetics and Breeding Faculty of Agrobiology and Natural Resources Czech University of Life Sciences Prague Prague 165 00 Czech Republic

Key Laboratory of Forest Genetics and Biotechnology Nanjing Forestry University Nanjing 210037 China

National Research Collection Australia CSIRO Canberra ACT 2601 Australia

RAGT Seeds Essex CB 101TA United Kingdom

Skogforsk Box 3 Sävar SE 918 21 Sweden

Umeå Plant Science Centre Department of Forest Genetics and Plant Physiology Swedish University of Agricultural Sciences Umeå SE 901 83 Sweden

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