Regions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights. In this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy. We investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights.

中文翻译：

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具有特定性状标记权重的计算上可行的多性状单步基因组预测模型


全基因组标记数据的区域可能对评估的性状有不同的影响。这可以通过为标记分配不同的权重来反映在基因组模型中，这可以提高基因组预测的准确性。然而，当允许性状具有不同的标记权重时，标准的多性状单步基因组评估模型在计算上可能不可行。在本研究中，我们开发并实施了用于大型基因组数据评估的多性状单步单核苷酸多态性最佳线性无偏预测（SNPBLUP）模型，该模型允许使用预先计算的性状特异性标记权重。对标准单步 SNPBLUP 模型的修改很小，并没有显着增加预处理工作量。该模型使用模拟数据和使用 BayesA 预先计算的标记权重进行测试。根据结果​​，与没有权重的标准单步模型相比，每次迭代的内存需求和计算时间略有增加。此外，使用标记权重时模型的收敛速度较慢，从而导致总计算时间更长。然而，标记权重的使用提高了预测准确性。我们研究了一个单步 SNPBLUP 模型，该模型可用于适应性状特异性标记权重。标记加权单步模型提高了预测准确性。该方法可用于使用预先计算的标记权重进行大型基因组数据评估。