This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel. The accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS $$- log_{10} (p\,value)$$ threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS $$- log_{10} (p\,value)$$ threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01). Our results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep.

中文翻译：

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使用估算的全基因组序列数据提高澳大利亚绵羊寄生虫抗性基因组预测的准确性

这项研究旨在（1）比较基于全基因组序列（WGS）数据与基于50k和高密度（HD）单核苷酸多态性（SNP）面板的绵羊的寄生虫抗性的基因组预测准确性；（2）调查在独立数据集中从区域性遗传图谱（RHM）中选择的定量性状基因位点（QTL）区域内的变体是否比从全基因组关联研究（GWAS）中选择的变体提高了准确性；（3）比较从WGS数据选择的变体与从HD SNP面板选择的变体之间的预测准确性。当使用来自50k和HD基因型的所有变体时，基因组预测的准确性分别从0.16±0.02和0.18±0.01略微提高到使用来自WGS数据的所有变体时的0.19±0.01。与仅拟合50k SNP基因型相比，将来自所选变体的GRM与来自50k SNP基因型的GRM进行拟合大大提高了预测准确性。当从WGS数据中选择变体时，与从HD面板中选择变体时相比，预测准确性的提高要明显得多。当序列变体传送通过GWAS $$ - LOG_ {10}（P \，值）$$选择在整个基因组的3阈值，提高了5％（高达0.21±0.01）的预测精度，而当选择被限定于通过同一GWAS $$序列变体 - LOG_ {10}（P \，值）$$在由RHM识别的区域的3阈值时，精度由9％改善（高达0.25±0.01） 。我们的结果表明，通过从QTL区域中精心选择序列变体，可以提高绵羊寄生虫抗性的基因组预测准确性。这些发现对绵羊的基因组预测具有重要意义。我们的结果表明，通过从QTL区域中精心选择序列变体，可以提高绵羊寄生虫抗性的基因组预测准确性。这些发现对绵羊的基因组预测具有重要意义。我们的结果表明，通过从QTL区域中精心选择序列变体，可以提高绵羊寄生虫抗性的基因组预测准确性。这些发现对绵羊的基因组预测具有重要意义。