Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

中文翻译：

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在严格选择的猪系中使用全基因组序列数据进行基因组预测

早期的模拟表明，全基因组序列数据（WGS）可以提高品种内和品种间基因组预测的准确性。然而，迄今为止，实证结果一直模棱两可。必须组装捕获群体中大部分基因组多样性的大型数据集，以便以高精度估计等位基因替代效应。本研究的目的是使用来自七个严格选择的品系的大型猪数据集来评估使用 WGS 进行基因组预测与使用商业标记阵列相比的优势，并确定 WGS 提供最大优势的场景。我们对来自 7 个不同数值大小的商业猪系的 6931 个个体进行了测序。对 396,100 个人（每行 17,224 到 104,661）估算了 3280 万个变异的基因型。我们使用 BayesR 对八种复杂性状进行基因组预测。使用来自标准标记阵列的数据或基于关联测试从 WGS 预选的变体进行基因组预测。基于预选 WGS 变体的基因组预测的准确性在性状和品系中并不稳健，并且与标准标记阵列相比，我们迄今为止使用 WGS 实现的预测准确性提高通常很小。当最大的训练集可用并且标准标记阵列增加了与性状具有统计学显着关联的预选变体时，获得了 WGS 最有利的结果。使用这种方法和大约 80k 个体的训练集，线内基因组预测的准确性平均提高了 0.025。使用多线训练集，与标记阵列相比，预计会有 0.04 的改进。我们的结果表明，与严格选择的猪系中的标记阵列相比，WGS 在提高基因组预测准确性方面的潜力有限。因此，尽管我们期望通过结合更大的训练集和用于生成和分析此类数据集的优化管道，使用 WGS 可以更大程度地提高准确性，但应仔细评估 WGS 在当前基因组预测实施中的使用根据具体情况计算大规模 WGS 数据的成本。