Evidence map›Paper›PMID 36130053›Full record

ArticleBioinformatics (Oxford, England)2022

EagleImp: fast and accurate genome-wide phasing and imputation in a single tool.

Lars Wienbrandt, David Ellinghaus

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lars WienbrandtInstitute of Clinical Molecular Biology, Kiel University, 24105 Kiel, Germany.ORCID 0000-0001-5685-2032
David EllinghausInstitute of Clinical Molecular Biology, Kiel University, 24105 Kiel, Germany.ORCID 0000-0002-4332-6110

Funding

Deutsche Forschungsgemeinschaft EL 831/3-1DFGDFG Cluster of Excellence 2167German Federal Ministry of Education and Research 01ZX1902APrecision Medicine in Chronic Inflammation
6 · The paper itself

Abstract

motivationReference-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.

resultsWe developed EagleImp, a software based on the methods used in the existing tools Eagle2 and PBWT, which allows accurate and accelerated phasing and imputation in a single tool by algorithmic and technical improvements and new features. 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 1 million reference genomes. EagleImp was 2-30 times faster (depending on the single or multiprocessor configuration selected and the size of the reference panel) than Eagle2 combined with PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical genome-wide association studies, EagleImp provided 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. Additional features include 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. Due to the technical optimizations, EagleImp can perform fast and accurate reference-based phasing and imputation and is ready for future large reference panels in the order of 1 million genomes. AVAILABILITY AND IMPLEMENTATION: EagleImp is implemented in C++ and freely available for download at https://github.com/ikmb/eagleimp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

GenomeGenome-Wide Association StudyGenotypeHaplotypesPolymorphism, Single NucleotideSoftware

Identifiers

PMID36130053
PMCPMC9665855

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.