Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine
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RESEARCH ARTICLE
Open Access
Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine Ainhoa Calleja-Rodriguez1,2 , Jin Pan2 , Tomas Funda2,3,4 , Zhiqiang Chen2 , John Baison2,5 , Fikret Isik6 , Sara Abrahamsson1 and Harry X. Wu2,7,8*
Abstract 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%. Keywords: Pinus sylvestris; genotyping-by-sequencing; GBLUP; Bayesian; predictive ability; predictive accuracy; theoretical accuracy; prediction accuracy
*Correspondence: [email protected] Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE - 901 83 Umeå, Sweden 7 Beijing Advanced Innovation Centre for Tree Breeding by Molecular Design, Beijing Forestry University, 100083 Beijing, China Full list of author information is available at the end of the article 2
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