Research article - Peer-reviewed, 2020
Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine
Calleja-Rodriguez, Ainhoa; Pan, Jin; Funda, Tomas; Chen, Zhiqiang; Baison, John; Isik, Fikret; Abrahamsson, Sara; Wu, Harry X.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 accuracyPublished in
BMC Genomics2020, volume: 21, number: 1, article number: 796
Publisher: BMC
Authors' information
Calleja-Rodriguez, Ainhoa
Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology
Calleja-Rodriguez, Ainhoa
Forestry Research Institute of Sweden, Skogforsk
Pan, Jin
Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology
Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology
Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology
Baison, John
Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology
Isik, Fikret
North Carolina State University (NC State)
Abrahamsson, Sara
Forestry Research Institute of Sweden, Skogforsk
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Swedish University of Agricultural Sciences, Department of Forest Genetics and Plant Physiology
Beijing Forestry University
UKÄ Subject classification
Forest Science
Publication Identifiers
DOI: https://doi.org/10.1186/s12864-020-07188-4
URI (permanent link to this page)
https://res.slu.se/id/publ/109746