Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform

Frances R Thistlethwaite,Charles Chen,Yousry A El-Kassaby,Ilga Porth,Blaise Ratcliffe,Michael U Stoehr,Jaroslav Klápště

doi:10.1186/s12864-017-4258-5

Abstract

BackgroundGenomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits. Here, we used: 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full-sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RR-BLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross-sites, multi-site, and multi-site to single site) and time (age-age/ trait-trait).ResultsThe RR-BLUP and GRR models produced similar predictive accuracies across the studied traits. Within-site GS prediction accuracies with models trained on EBVs were high (RR-BLUP: 0.79–0.91 and GRR: 0.80–0.91), and were generally similar to the multi-site (RR-BLUP: 0.83–0.91, GRR: 0.83–0.91) and multi-site to single-site predictive accuracies (RR-BLUP: 0.79–0.92, GRR: 0.79–0.92). Cross-site predictions were surprisingly high, with predictive accuracies within a similar range (RR-BLUP: 0.79–0.92, GRR: 0.78–0.91). Height at 12 years was deemed the earliest acceptable age at which accurate predictions can be made concerning future height (age-age) and wood density (trait-trait). Using DEBVs reduced the accuracies of all cross-validation procedures dramatically, indicating that the models were tracking pedigree (family means), rather than marker-QTL LD.ConclusionsWhile GS models’ prediction accuracies were high, the main driving force was the pedigree tracking rather than LD. It is likely that many more markers are needed to increase the chance of capturing the LD between causal genes and markers.

Highlights

Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits
The only differences occurring in the prediction of genomic estimated breeding values (GEBVs) for HT35, in which the GRR method produced slightly lower and generalized ridge regression (GRR)) models for Estimated breeding value (EBV) and GEBVs of heights (HT12 and HT35) and wood density (WDres)
The results suggest that the population of SNP markers used, along with their low coverage across the Douglas-fir genome was not successful for capturing the linkage disequilibrium (LD) with the causal genes underpinning the studied attributes

Summary

Introduction

Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits. The resulting genomic estimated breeding values (GEBVs) for each individual derived from the GS models provide a basis, upon which selection decisions are made. The effect of this is a paradigm shift, in which the model unit of these breeding analyses shifts from being the line of decent to the allele. This means that the phenotypic values of individuals are determined from genotypic data, enabling early selection of traits, leading to a significantly shorter breeding cycle and higher selection differential, for the “difficult to assess” attributes

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Discussion

Conclusion