使用高质量和公开可用的牛参考小组对低覆盖率全基因组测序数据进行有效的分阶段和插补

Zhuangbiao Zhang, Ao Wang, Honghong Hu, Lulu Wang, Mian Gong, Qimeng Yang, Anguo Liu, Ran Li, Huanhuan Zhang, Qianqian Zhang, Ali Mujtaba Shah, Xihong Wang, Yachun Wang, Quanzhong Liu, Liutao Gao, Zhipeng Zhang, Congyong Wang, Yun Ma, Yudong Cai, Yu Jiang
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摘要

低覆盖率全基因组测序(lcWGS)在有效地对大规模人群进行基因分型和提供可靠的插补数据方面具有巨大的潜力;然而,插补时间需要优化。牛的全基因组选择也没有公开的参考小组。在这里,我们提出了Beagle v5.4用于定相和GLIMPSE2用于插补的组合,这对于牛的lcWGS数据来说是快速准确的。此外,我们通过评估参考小组的多样性和规模,以2976头全球牛为基础,建立了一个拥有6180万个SNP的多品种参考小组,其中1766头是公牛。使用lcWGS和牛BeadChip数据的新参考面板对插补准确性进行评估。Holstein的1X、0.5X和0.1X lcWGS数据的平均一致率分别为99.6%、99.6%和99.5%,777K和50K芯片数据的平均符合率分别为99.5%和99.0%,Simmental的1X lcWGS为98.8%。我们进一步调查了影响lcWGS数据插补准确性的因素,发现节段重复、结构变异和鸟嘌呤胞嘧啶含量是前三个因素。有趣的是,我们发现,10个长度超过0.5Mb的区域显示出低的插补准确性,富含免疫功能,例如96.1%的特征基因位于10号染色体区域,更多地关注下游免疫相关分析。我们的研究提供了输入lcWGS数据的工作流程,并建立了第一个免费访问的高质量牛参考小组,为随后进行大规模全基因组关联研究和基因组选择提供了资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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The efficient phasing and imputation pipeline of low-coverage whole genome sequencing data using a high-quality and publicly available reference panel in cattle

Low-coverage whole genome sequencing (lcWGS) has great potential to effectively genotype large-scale population and to provide solid data for imputation; however, the time for imputation needs to be optimized. There is also no publicly available reference panel for whole genome selection in cattle. Here, we proposed a combination of Beagle v5.4 for phasing and GLIMPSE2 for imputation, which is fast and accurate for cattle lcWGS data. Furthermore, we established a multi-breed reference panel with 61.8 million SNPs based on 2976 worldwide cattle, of which 1766 were bulls, by evaluating diversity and the size of the reference panel. The evaluation of imputation accuracy was conducted using new reference panel for both lcWGS and Bovine BeadChip data. The average concordance rate in Holstein was 99.6%, 99.6%, and 99.5% for 1X, 0.5X, and 0.1X lcWGS data, 99.5% and 99.0% for 777K and 50K chip data, and it was 98.8% for 1X lcWGS data in Simmental. We further investigated the factors affecting the imputation accuracy of lcWGS data and discovered that segmental duplication, structural variant, and guanine-cytosine content were the top three factors. Interestingly, we found that 10 regions longer than 0.5 Mb showed low imputation accuracy enriched with immune function, such as 96.1% characterized genes in regions of chromosome 10, with more attention being paid on downstream immune-related analysis. Our study provides the workflow of imputing lcWGS data and establishes the first high-quality cattle reference panel with free access, which provides a resource to conduct subsequent large-scale genome-wide association studies and genomic selection.

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