Low-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panels and their interplay with the coverage of low-pass whole-genome sequencing data have not been sufficiently explored in typical livestock settings where only a small number of reference samples is available. Sequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4- to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant based on 501 individuals from nine breeds. High-coverage sequence data for 24 Brown Swiss cattle were downsampled to between 0.01- and 4-fold to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes that were inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data. DeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Within-breed haplotype reference panels were more accurate and efficient to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, which indicate high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large within-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low-pass sequencing was substantially less accurate when the reference panel lacked individuals from the target breed. Variant genotyping is more accurate with DeepVariant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.

中文翻译：

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单倍型参考面板的大小和组成影响牛低通测序归集的准确性

低通测序后接序列变异基因型插补是牛常规基于微阵列的基因分型的替代方法。然而，单倍型参考面板的影响及其与低通全基因组测序数据覆盖范围的相互作用尚未在只有少量参考样本可用的典型牲畜环境中得到充分探索。在 50 头 Brown Swiss 牛中比较了两个变异调用者 GATK 和 DeepVariant 的序列变异基因分型准确性，测序覆盖范围为 4 到 63 倍。不同大小和组成的单倍型参考面板是使用 DeepVariant 基于来自 9 个品种的 501 个个体构建的。24 只瑞士布朗牛的高覆盖序列数据被下采样到 0.01 到 4 倍之间以模拟低通测序。GLIMPSE 用于使用不同的单倍型参考面板从低通测序数据推断序列变异基因型。将从低通测序数据推断出的序列变异基因型的准确性与从高覆盖数据中调用的序列变异基因型进行比较。DeepVariant 用于建立牛单倍型参考面板，因为它在所有评估中都优于 GATK。对于所有目标样本覆盖率和等位基因频率，品种内单倍型参考面板比同等大小的多品种单倍型参考面板更准确、更有效地从低通测序推算序列变异基因型。F1 分数大于 0.9，这表明被调用基因型的召回和精确度的调和平均值为 0。使用大型品种特异性单倍型参考组 (n = 150) 时，测序覆盖率为 25 倍。在没有如此大的品种内单倍型面板的情况下，可以通过将非相关样本添加到单倍型参考面板或通过增加低通测序数据的覆盖范围来提高低通测序的变异基因分型准确性。当参考小组缺乏来自目标品种的个体时，低通测序的序列变异基因分型准确度大大降低。DeepVariant 的变异基因分型比 GATK 更准确。因此，DeepVariant 适合建立牛单倍型参考组。中型品种特异性单倍型参考面板和大型多品种单倍型参考面板能够准确估算典型牛品种的低通测序数据。