Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia.
School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
Genet Sel Evol. 2019 Jun 26;51(1):32. doi: 10.1186/s12711-019-0476-4.
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 [Formula: see text] 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 [Formula: see text] 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.
本研究旨在：（1）比较基于全基因组序列（WGS）数据和 50k 及高密度（HD）单核苷酸多态性（SNP）面板的基因组预测对绵羊寄生虫抗性的准确性；（2）调查在独立数据集的区域遗传力图谱（RHM）中选择的数量性状基因座（QTL）区域内的变体是否比从全基因组关联研究（GWAS）中选择的变体更能提高准确性；（3）比较从 WGS 数据中选择的变体和从 HD SNP 面板中选择的变体的预测准确性。
当使用所有来自 50k 和 HD 基因型的变体时，基因组预测的准确性分别从 0.16±0.02 和 0.18±0.01 略有提高到 0.19±0.01，当使用所有来自 WGS 数据的变体时。与仅拟合 50k SNP 基因型相比，拟合从选定变体构建的 GRM 以及从 50k SNP 基因型构建的 GRM 可以大大提高预测准确性。当从 WGS 数据中选择变体而不是从 HD 面板中选择变体时，预测准确性的提高幅度略高。当从整个基因组中通过全基因组关联研究（GWAS）[公式：见正文]阈值 3 筛选通过的序列变体时，预测准确性提高了 5%（最高可达 0.21±0.01），而当选择仅限于通过 RHM 确定的区域中通过相同 GWAS [公式：见正文]阈值 3 的序列变体时，准确性提高了 9%（最高可达 0.25±0.01）。
我们的结果表明，通过从 QTL 区域仔细选择序列变体，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。
