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 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 (~25kb) 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.

本研究针对一个远交F2杂交桉树群体（n=768），采用高密度单核苷酸多态性（Single Nucleotide Polymorphism, SNP）分型技术，开展了生长与木材品质性状的基因组选择（Genomic Selection, GS）研究。相较于以往林木领域的相关研究，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、家系内个体以及全群体内个体的选择模型，所采用的基因组模型纳入了显性效应。为了给育种者提供更易理解的基因组选择性能评估，本研究采用严格的交叉验证（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——针对不同比例的基因组选择个体，将预期响应值定义为相较于群体平均预期遗传值（Expected Genetic Value, EGV）的百分比增益。预测能力（Predictive Abilities, PAs）在个体选择中为0.40~0.57，在家系选择中为0.56~0.75。采用加性+显性模型时，针对不同选择目标，生长性状的预测能力提升了5%~14%，但木材性状的预测能力未出现显著提升。交叉验证中未引入亲缘关系仍可实现优异的基因组选择效果，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, LD）。截断式基因组选择（Truncation GS）成功筛选出了平均预期遗传值显著高于群体平均水平的个体。以年度为基准计算，基因组选择的效率约为表型选择的2倍，且选择强度越高，该优势越显著。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的论断。鉴于林木世代周期较长、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应用前景。