ArticleBMC genomics2017
Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Genomic models for accurate estimation of breeding values in Sitka spruce (Picea sitchensis Bong. Carr).Heredity · 2026Article
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- Application and development prospect of genomic selection breeding in coniferous trees.Planta · 2025Review
- Cross-generational genomic prediction of Norway spruce (Picea abies) wood properties: an evaluation using independent validation.BMC genomics · 2025Article
- Uncovering epigenetic and transcriptional regulation of growth in Douglas-fir: identification of differential methylation regions in mega-sized introns.Plant biotechnology journal · 2024Article
- Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch.BMC genomics · 2024Article
- Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce.BMC genomics · 2023Article
- Realized genomic selection across generations in a reciprocal recurrent selection breeding program ofFrontiers in plant science · 2023Article
- Metadata analysis indicates biased estimation of genetic parameters and gains using conventional pedigree information instead of genomic-based approaches in tree breeding.Scientific reports · 2022Article
- Identifying and testing marker-trait associations for growth and phenology in three pine species: Implications for genomic prediction.Evolutionary applications · 2022Article
- Integrating genomic information and productivity and climate-adaptability traits into a regional white spruce breeding program.PloS one · 2022Article
- Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing.International journal of molecular sciences · 2021Review
- Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance.Genome biology · 2021Article
- Looking for the needle in a downsized haystack: Whole-exome sequencing unravels genomic signals of climatic adaptation in Douglas-fir (Ecology and evolution · 2021Article
- Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values inFrontiers in plant science · 2021Article
- Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine.BMC genomics · 2020Article
- Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population ofG3 (Bethesda, Md.) · 2020Article
- Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight.BMC plant biology · 2020Article
- Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce.BMC genomics · 2020Article
- Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model SpeciesPlants (Basel, Switzerland) · 2020Article
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7 authors.
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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.
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