Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine. Calleja-Rodriguez, A., Pan, J., Funda, T., Chen, Z., Baison, J., Isik, F., Abrahamsson, S., & Wu, H. X. BMC Genomics, 21(1):796, December, 2020.
Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine [link]Paper  doi  abstract   bibtex   1 download  
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%.
@article{calleja-rodriguez_evaluation_2020,
	title = {Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in {Scots} pine},
	volume = {21},
	issn = {1471-2164},
	url = {https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07188-4},
	doi = {10.1186/s12864-020-07188-4},
	abstract = {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\%.},
	language = {en},
	number = {1},
	urldate = {2021-06-07},
	journal = {BMC Genomics},
	author = {Calleja-Rodriguez, Ainhoa and Pan, Jin and Funda, Tomas and Chen, Zhiqiang and Baison, John and Isik, Fikret and Abrahamsson, Sara and Wu, Harry X.},
	month = dec,
	year = {2020},
	pages = {796},
}
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