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杂交桉树群体（样本量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）基因分型技术。相较于已有林木相关研究报道，本研究针对三类不同选择目标构建了包含显性效应的基因组选择模型，分别为家系选择、家系内个体选择以及全群体范围内的个体选择。为便于育种者更直观地评估基因组选择的表现，本研究采用了严格的交叉验证（cross-validation, CV）方案——该方案会排除训练集与验证集间的亲缘关系——并针对不同基因组选择个体比例，计算了其相对于群体平均预期遗传值（expected genetic value, EGV）的增益百分比，以此作为预期响应。个体选择的预测能力（predictive abilities, PAs）区间为0.40~0.57，家系选择的预测能力区间为0.56~0.75。相较于仅加性模型，纳入显性效应的加性+显性模型可使不同选择目标下的生长性状预测能力提升5%~14%，但木材性状的预测能力未出现显著提升。交叉验证中无亲缘关系限制的基因组选择表现优异，表明本研究采用的平均SNP密度（约25 kb）捕获了部分短程连锁不平衡。截断式基因组选择成功筛选出平均预期遗传值显著高于群体平均水平的个体。单位时间内基因组选择的响应效率约为表型选择的2倍，且选择强度越高，该效率优势越明显。本研究结果进一步提供了实验证据，支持基因组选择在林木育种中具备良好应用前景。鉴于林木世代周期长、性状复杂且DNA基因分型成本持续下降，基因组预测在林木育种实践中拥有广阔的推广前景。