Data from: Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model

We report a genomic selection (GS) study of growth and wood quality traits in an outbred F2 hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40–0.57 for individual selection and 0.56–0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.本研究针对远交F2杂交桉种群（n=768）开展生长与木材品质性状的基因组选择（genomic selection, GS）研究，采用高密度单核苷酸多态性（single-nucleotide polymorphism, SNP）分型技术。相较于以往林木相关研究，本研究针对不同选择目标构建了基因组选择模型，具体涵盖家系、家系内个体以及全种群范围内的个体，模型纳入了显性效应。为便于育种工作者更清晰地评估基因组选择的表现，我们采用严谨的交叉验证（cross-validation, CV）方案——该方案剔除了训练集与验证集之间的亲缘关系——计算了不同基因组选择个体比例下，相较于种群平均预期遗传值（expected genetic value, EGV）的预期增益百分比。针对个体选择，预测能力（predictive abilities, PAs）介于0.40~0.57之间；针对家系选择，预测能力则为0.56~0.75。相较于仅包含加性效应的模型，纳入加性+显性效应的模型对生长性状的预测精度提升了5%~14%，但对木材性状无显著改善。交叉验证中未引入亲缘关系的基因组选择仍表现优异，这表明本研究的平均SNP密度（约25kb）捕捉到了一定程度的短程连锁不平衡（linkage disequilibrium）。截断式基因组选择成功筛选出平均预期遗传值显著高于种群平均水平的个体。单位年度的基因组选择响应效率约为表型选择的2倍，且选择强度越高，该优势愈发明显。本研究结果为林木基因组选择的应用前景提供了进一步的实验支撑。鉴于林木世代周期长、性状复杂且DNA分型成本持续走低，基因组预测在林木育种实践中具备良好的推广潜力。