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

BMC Genomics

Research Article

Michael U. Stoehr¹  Charles Chen²  Frances R. Thistlethwaite³  Blaise Ratcliffe³  Yousry A. El-Kassaby³  Jaroslav Klápště⁴  Ilga Porth⁵ 

[1] British Columbia Ministry of Forests, Lands and Natural Resource Operations, V8W 9C2, Victoria, BC, Canada;Department of Biochemistry and Molecular Biology, Oklahoma State University, 74078-3035, Stillwater, OK, USA;Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, V6T 1Z4, Vancouver, BC, Canada;Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, V6T 1Z4, Vancouver, BC, Canada;Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Whakarewarewa, 3046, Rotorua, New Zealand;Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycka 129, 165 21, Praha 6, Czech Republic;Département des sciences du bois et de la forêt, Université Laval, G1V 0A6, QC, Québec, Canada;

关键词: Douglas-fir;    Genomic selection;    Exome capture;    Full-sib families;    Genotype x environment interaction;    Predictive model;   

DOI  :  10.1186/s12864-017-4258-5

 received in 2017-03-28, accepted in 2017-11-01,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017

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