Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

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%.

Identifying and testing marker-trait associations for growth and phenology in three pine species: implications for genomic prediction

“…We found, as has been previously reported (Calleja-Rodriguez et al 2020), that predictive ability in P. sylvestris (estimated as the correlation between the genomic estimated breeding values and phenotypes) was positively associated with narrow sense heritability of the trait. In contrast, the predictive ability of the models in an independent multi-site trial was not correlated with the predictive ability in the association dataset, possibly because of the different environments involved.…”

“…In pines, association studies and tests of genomic prediction have been performed for serotiny ( Pinus pinaster , Parchman et al 2012; Budde et al, 2014), circumference, height, stem straightness ( Pinus pinaster , Bartholomé et al, 2016), oleoresin flow ( Pinus taeda , Westbrook et al, 2013) and growth and wood quality traits ( Pinus. sylvestris , Calleja-Rodriguez et al, 2020).…”

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

“…(Ukrainetz and Mansfield, 2020). Given that the correlation between an individual phenotype and its true breeding value cannot be larger than the square root of heritability, r 2 is recognized as an unbiased estimation of accuracy of selection from n-fold-cross validation (Legarra et al, 2008;Meuwissen et al, 2013), thus r 2 has begun to be more standard used in forest tree GS studies (Lenz et al, 2019(Lenz et al, , 2020Calleja-Rodríguez et al, 2020;Klápšte et al, 2020;Pégard et al, 2020;Zhou et al, 2020). Similar r 1 were observed, for all models and traits evaluated for Scots pine, which agrees with previous studies (Bouvet et al, 2016;de Almeida Filho et al, 2016;Chen et al, 2019), but differs from other eucalyptus reports in which the genomic models performed better (Tan et al, 2018), yet in the same study inconsistencies in the original pedigree were detected, and a pseudo-pedigree was used.…”

Taming the massive genome of Scots pine with PiSy50k, a new genotyping array for conifer research

“…Genomic estimated breeding values (GEBV) can then be calculated from this equation for individuals with genotype data only (e.g., Wray et al ., 2019). GS in trees shows promising results (Isik 2014) and good predictive ability has been achieved with a few thousand of SNPs (e.g., Bartholomé et al ., 2016; Calleja-Rodriguez et al ., 2020; Cappa et al ., 2019; Chen et al ., 2018; Grattapaglia et al ., 2018; Lenz et al ., 2017; Resende et al ., 2012; but see Thistlethwaite et al ., 2020).…”

“…There are significant biological constraints to achieve this in Scots pine that reaches sexual maturity at 8-20 years of age (Sarvas 1964). Nevertheless, genomic markers can provide other benefits by reducing the phenotyping costs and achieving higher selection intensities in situations when a large number of selection candidates are more easily genotyped than phenotyped (Calleja-Rodriguez et al ., 2020; Grattapaglia et al ., 2018; Voss-Fels et al ., 2019). The operational viability of such measures is obviously dependent on the costs of genotyping.…”