Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait

In prediction of genomic values, single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of the model integrating both single-step and RR-TDM prediction methods, called single-step random regression test-day model (SS RR-TDM), in comparison with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit potential advantages of SS RR-TDM over the other two models under various scenarios with different level of heritability, the number of QTL as well as the selection scheme. SS RR-TDM was found to achieve the highest accuracy and unbiasedness under all scenarios, exhibiting robust prediction ability in longitudinal trait analyses. Moreover, SS RR-TDM showed better persistency of accuracy over generations than GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of a real dataset of human contributed by the GAW18 workshop. The findings in our study firstly proved the feasibility and advantages of the SS RR-TDM, and further enhanced strategies for the genomic prediction of longitudinal traits in the future.

在基因组值预测领域，单步法（single-step method）已被证实优于多步法。针对纵向性状的统计分析，随机回归测定日模型（random regression test-day model, RR-TDM）相较其他模型具有显著优势。本研究旨在评估整合单步法与RR-TDM预测方法的模型——单步随机回归测定日模型（single-step random regression test-day model, SS RR-TDM）的性能，并将其与基于系谱的RR-TDM及基因组最佳线性无偏预测（genomic best linear unbiased prediction, GBLUP）模型进行对比。我们开展了大规模模拟试验，以探究SS RR-TDM在不同遗传力、数量性状基因座（quantitative trait locus, QTL）数目及选择方案的多种场景下，相较于另外两种模型的潜在优势。结果显示，SS RR-TDM在所有测试场景中均实现了最高的预测准确性与无偏性，展现出适用于纵向性状分析的稳健预测能力。此外，相较于GBLUP模型，SS RR-TDM在跨世代间的准确性持续性更优。基于GAW18研讨会（GAW18 workshop）提供的人类真实数据集，我们同样证实SS RR-TDM相较于RR-TDM与GBLUP模型具备优势。本研究首次验证了SS RR-TDM的可行性与应用优势，可为未来纵向性状的基因组预测策略提供优化方向。