BACKGROUND During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis. RESULTS We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K. CONCLUSIONS Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

中文翻译：

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多品种参考样本和其他稀有变种最大程度地提高了牛的插补准确性。

背景技术在过去的十年中，在牛肉和奶制品行业中使用基于公共变量数组的单核苷酸多态性（SNP）基因分型产生了惊人数量的中低密度基因组数据。尽管低密度测定法在基因组预测的背景下效果很好，但是它们对于检测和绘制因果变体的作用较小，并且未捕获稀有变体的影响。该项目的目的是最大程度地提高从中密度检测和低密度检测到通过结合两种高密度研究检测（〜850,000个SNP），Illumina BovineHD和GGP-F250检测获得的标记集的基因型推算的准确性，其中包含大量稀有和可能具有功能的变体，此处介绍了其检测设计。这套850 K SNP集可用于序列水平基因型的推算和直接下游分析。结果我们发现，一个大型的多品种复合插补参考标本，包括36131个具有BovineHD和/或GGP-F250基因型的样本，与内部参考标本相比，显着提高了插补准确性，尤其是在等位基因频率较低的变体中。当更多的遗传相似的动物出现在复合参考组中时，尤其是具有完整的850 K基因型的个体，动物个体的估算准确性就达到了最大化。将GGP-F250分析中的稀有变体添加到我们的复合参考面板中，显着提高了BovineHD分析中独有的稀有变体的插补准确性。此外，我们显示了50 K SNPs的测定标记密度可以平衡估算到850 K的成本和准确性。结论在多品种参考面板上的所有可用个体上使用高密度基因型，可以最大化被测牛种群的估算准确性。仍会以较高的准确性估算混合动物或来自混合参考面板中代表性有限的品种的动物，随着参考面板的扩展，预计还会进一步增加。我们预计，从GGP-F250分析中添加稀有变体会增加归因于序列水平的准确性。仍会以较高的准确性估算混合动物或来自在复合参考面板中代表性有限的品种的动物，随着参考面板的扩展，预计该动物将进一步增加。我们预计，从GGP-F250测定法中添加稀有变体将增加归因于序列水平的准确性。仍会以较高的准确性估算混合动物或来自在复合参考面板中代表性有限的品种的动物，随着参考面板的扩展，预计该动物将进一步增加。我们预计，从GGP-F250测定法中添加稀有变体将增加归因于序列水平的准确性。