Different SNP genotyping technologies are commonly used in multiple studies to perform QTL detection, genotype imputation, and genomic predictions. Therefore, genotyping errors cannot be ignored, as they can reduce the accuracy of different procedures applied in genomic selection, such as genomic imputation, genomic predictions, and false-positive results in genome-wide association studies. Currently, whole-genome resequencing (WGR) also offers the potential for variant calling analysis and high-throughput genotyping. WGR might overshadow array-based genotyping technologies due to the larger amount and precision of the genomic information provided; however, its comparatively higher price per individual still limits its use in larger populations. Thus, the objective of this work was to evaluate the accuracy of the two most popular SNP-chip technologies, namely, Affymetrix and Illumina, for high-throughput genotyping in sheep considering high-coverage WGR datasets as references. Analyses were performed using two reference sheep genome assemblies, the popular Oar_v3.1 reference genome and the latest available version Oar_rambouillet_v1.0. Our results demonstrate that the genotypes from both platforms are suggested to have high concordance rates with the genotypes determined from reference WGR datasets (96.59% and 99.51% for Affymetrix and Illumina technologies, respectively). The concordance results provided in the current study can pinpoint low reproducible markers across multiple platforms used for sheep genotyping data. Comparing results using two reference genome assemblies also informs how genome assembly quality can influence genotype concordance rates among different genotyping platforms. Moreover, we describe an efficient pipeline to test the reliability of markers included in sheep SNP-chip panels against WGR datasets available on public databases. This pipeline may be helpful for discarding low-reliability markers before exploiting genomic information for gene mapping analyses or genomic prediction.

中文翻译：

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绵羊不同SNP基因分型平台的一致性研究

多项研究通常使用不同的 SNP 基因分型技术来进行 QTL 检测、基因型插补和基因组预测。因此，基因分型错误不容忽视，因为它们会降低应用于基因组选择的不同程序的准确性，例如基因组插补、基因组预测和全基因组关联研究中的假阳性结果。目前，全基因组重测序 (WGR) 还提供了变异调用分析和高通量基因分型的潜力。由于提供的基因组信息的数量和精度更高，WGR 可能会掩盖基于阵列的基因分型技术；然而，其相对较高的人均价格仍然限制了它在更多人群中的使用。因此，这项工作的目的是评估两种最流行的 SNP 芯片技术，即 Affymetrix 和 Illumina，以高覆盖率 WGR 数据集作为参考，对绵羊进行高通量基因分型的准确性。使用两个参考绵羊基因组组装进行分析，流行的 Oar_v3.1 参考基因组和最新的可用版本 Oar_rambouillet_v1.0。我们的结果表明，来自两个平台的基因型与从参考 WGR 数据集确定的基因型具有较高的一致性（Affymetrix 和 Illumina 技术分别为 96.59% 和 99.51%）。当前研究中提供的一致性结果可以在用于绵羊基因分型数据的多个平台上确定低重现性标记。比较使用两个参考基因组组装的结果还可以说明基因组组装质量如何影响不同基因分型平台之间的基因型一致性率。此外，我们描述了一种有效的管道，用于根据公共数据库上可用的 WGR 数据集测试绵羊 SNP 芯片面板中包含的标记的可靠性。该管道可能有助于在利用基因组信息进行基因图谱分析或基因组预测之前丢弃低可靠性标记。