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

AbstractBackground 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. 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). Results The 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. Conclusions While 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., Usage notesDouglas-fir exomic SNP fileExomic genotype file for Douglas-fir produced by RAPiD Genomics© , containing 74199 biallelic SNPs with less than 40% missing data and minQ=10.FiltStep1_minQ10_Gen.txtDouglas-fir phenotypesPhenotypic measurements from Douglas-fir trial over 3 sites in British Columbia, Canada. Courtesy of Forests, Lands and Natural Resource Operations, BC, Canada.DF_phenotypes_dryad.txt

**摘要与背景** 基因组选择（Genomic Selection, GS）可在成本效率与世代周转方面为林木选择育种带来前所未有的遗传增益，尤其针对晚表达性状与低遗传力性状。本研究采用两种实验设计：1）以外显子组捕获（exome capture）作为基因分型平台，对加拿大不列颠哥伦比亚省3个试验点的1372株花旗松（Douglas-fir）进行基因分型，试材涵盖37个全同胞家系；2）以树高生长性状、木材密度的估计育种值（Estimated Breeding Values, EBVs）以及去校正估计育种值（Deregressed Estimated Breeding Values, DEBVs）作为表型数据，其中EBVs与DEBVs分别代表带有系谱结构与不带系谱结构的模型。本研究采用岭回归最佳线性无偏预测（Ridge Regression Best Linear Unbiased Predictor, RR-BLUP）与广义岭回归（Generalized Ridge Regression, GRR）两种模型，从空间维度（单试验点内、跨试验点、多试验点、多试验点到单试验点）与时间维度（年龄-年龄预测、性状-性状预测）评估模型的预测准确性。

**结果** 针对本研究中的所有性状，RR-BLUP与GRR模型的预测准确性相近。以EBVs为训练集的单试验点基因组选择预测准确性较高（RR-BLUP：0.79~0.91，GRR：0.80~0.91），整体与多试验点预测准确性（RR-BLUP：0.83~0.91，GRR：0.83~0.91）以及多试验点到单试验点的预测准确性（RR-BLUP：0.79~0.92，GRR：0.79~0.92）相当。跨试验点预测准确性同样出人意料地高，准确率处于相近区间（RR-BLUP：0.79~0.92，GRR：0.78~0.91）。12年树高被认为是开展后续树高（年龄-年龄预测）与木材密度（性状-性状预测）准确预测的最早可接受树龄。使用DEBVs作为表型数据会显著降低所有交叉验证程序的预测准确性，表明模型拟合的是系谱信息（家系均值），而非标记与数量性状基因座（Quantitative Trait Locus, QTL）之间的连锁不平衡（Linkage Disequilibrium, LD）。

**结论** 尽管基因组选择模型的预测准确性较高，但其主要驱动因素为系谱信息拟合，而非连锁不平衡。未来或需更多的分子标记，才能提升捕获功能基因与标记间连锁不平衡的概率。

**使用说明**
1. 花旗松外显子组SNP文件：由RAPiD Genomics© 公司制备的花旗松外显子组基因型文件，包含74199个双等位基因单核苷酸多态性（Single Nucleotide Polymorphism, SNP），缺失数据占比低于40%，最低质量值minQ=10。对应文件：FiltStep1_minQ10_Gen.txt
2. 花旗松表型数据：加拿大不列颠哥伦比亚省3个试验点的花旗松试验林表型测定数据，由不列颠哥伦比亚省森林、土地与自然资源运营局（Forests, Lands and Natural Resource Operations, BC, Canada）提供。对应文件：DF_phenotypes_dryad.txt