For assembling large whole-genome sequence datasets for routine use in research and breeding, the sequencing strategy should be adapted to the methods that will be used later for variant discovery and imputation. In this study, we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method that is well suited for large livestock populations. We simulated marker array and whole-genome sequence data for 15 populations with simulated or real pedigrees that had different structures. In these populations, we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population, we considered four levels of investment in sequencing that were proportional to the size of the population. Imputation accuracy depended greatly on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence and it was critical for achieving high imputation accuracy in both early and late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of 2× rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2× provided high imputation accuracy. The gain in imputation accuracy from additional investment decreased with larger populations and higher levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones. Suitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing ~2% of the population at a uniform coverage 2×, distributed preferably across all generations of the pedigree, except for the few earliest generations that lack genotyped ancestors. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

中文翻译：

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混合剥离全基因组插补测序策略的评估


为了组装大型全基因组序列数据集以供研究和育种常规使用，测序策略应适应稍后用于变异发现和估算的方法。在本研究中，我们使用模拟来探索测序策略和测序投资水平对混合剥皮插补整体准确性的影响，混合剥皮是一种基于谱系的插补方法，非常适合大型牲畜种群。我们模拟了 15 个具有不同结构的模拟或真实谱系的群体的标记阵列和全基因组序列数据。在这些人群中，我们评估了七种方法对估算准确性的影响，这些方法用于选择要测序的个体、测序个体所属谱系的生成、可变或均匀覆盖范围的使用以及个体数量之间的权衡。已测序的个体及其测序覆盖率。对于每个群体，我们考虑了与群体规模成正比的四个测序投资水平。插补的准确性在很大程度上取决于谱系深度。谱系各代中已测序个体的分布是用于选择要测序的个体的不同方法的性能的基础，这对于在早期和晚期世代中实现高插补准确性至关重要。 2× 的测序个体的统一覆盖率而不是可变覆盖率的插补精度最高。相当于对 2% 的人群进行 2 倍测序的成本的投资提供了很高的插补准确性。随着人口数量的增加和投资水平的提高，额外投资所带来的估算准确性的提高会降低。 然而，为了达到相同的插补精度，与较大人群相比，较小人群必须按比例增加投资。用于后续混合剥离插补的合适测序策略包括以均匀覆盖率 2x 对约 2% 的群体进行测序，最好分布在谱系的所有世代中，除了缺乏基因型祖先的少数最早的世代之外。这种测序策略有利于在具有密切相关个体的深厚谱系的群体中生成全基因组序列数据。