EagleImp: fast and accurate genome-wide phasing and imputation in a single tool
EagleImp: fast and accurate genome-wide phasing and imputation in a single tool
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DOI：
10.1101/2022.01.11.475810
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发表时间：
2022-01
期刊：
影响因子：
5.8
通讯作者：
Lars Wienbrandt;D. Ellinghaus
中科院分区：
文献类型：
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作者：
Lars Wienbrandt;D. Ellinghaus
背景 基于参考的定相和基因型插补算法已经开发出亚线性理论运行时行为，但在使用大型全基因组参考数据集时，实际运行时间仍然很高。方法 我们开发了 EagleImp，这是一款在算法和技术方面进行改进的软件，并具有新功能，可在单一工具中实现准确和加速的定相和插补。结果 我们使用来自 1000 个基因组计划、Haplotype Reference Consortium 的全基因组测序数据以及超过 100 万个参考基因组的模拟数据，将 EagleImp 与 Eagle2、PBWT 和著名插补服务器的准确性和运行时间进行了比较。 EagleImp 的速度比 Eagle2/PBWT 快 2 到 10 倍（取决于所选的单处理器或多处理器配置），并且在所有测试场景中具有相同或更好的定相和插补质量。对于典型 GWAS 研究中调查的常见变异，EagleImp 提供了与 Sanger Imputation Service、Michigan Imputation Server 和新开发的 TOPMed Imputation Server 相同或更高的插补精度，尽管参考面板更大（未公开）。它具有许多新功能，包括运行时自动染色体分裂和内存管理以避免作业中止、快速读写大文件以及各种用户可配置的算法和输出选项。结论 由于技术优化，EagleImp 可以对未来具有超过 100 万个基因组的超大型参考组执行快速、准确的基于参考的定相和插补。 EagleImp 可以从 https://github.com/ikmb/eagleimp 免费下载。
Background Reference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used. Methods We developed EagleImp, a software with algorithmic and technical improvements and new features for accurate and accelerated phasing and imputation in a single tool. Results We compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with more than 1 million reference genomes. EagleImp is 2 to 10 times faster (depending on the single or multiprocessor configuration selected) than Eagle2/PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical GWAS studies, EagleImp provides same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. It has many new features, including automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files, and various user-configurable algorithm and output options. Conclusions Due to the technical optimisations, EagleImp can perform fast and accurate reference-based phasing and imputation for future very large reference panels with more than 1 million genomes. EagleImp is freely available for download from https://github.com/ikmb/eagleimp.