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Genotype imputation using the Positional Burrows Wheeler Transform

View ORCID ProfileSimone Rubinacci, View ORCID ProfileOlivier Delaneau, View ORCID ProfileJonathan Marchini
doi: https://doi.org/10.1101/797944
Simone Rubinacci
Department of Statistics, University of Oxford, Oxford, UK
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Olivier Delaneau
Department of Computational Biology, University of Lausanne, Lausanne, SwitzerlandSwiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
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Jonathan Marchini
Regeneron Genetics Center, Tarrytown, NY, USA
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  • For correspondence: jonathan.marchini@regeneron.com
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Abstract

Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. Increasing reference panel size poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. It achieves fast and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT), which are used as conditioning states within the IMPUTE model. IMPUTE5 is 20x faster than MINIMAC4 and 3x faster than BEAGLE5, when using the HRC reference panel, and uses less memory than both these methods. IMPUTE5 scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 09, 2019.
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Genotype imputation using the Positional Burrows Wheeler Transform
Simone Rubinacci, Olivier Delaneau, Jonathan Marchini
bioRxiv 797944; doi: https://doi.org/10.1101/797944
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Genotype imputation using the Positional Burrows Wheeler Transform
Simone Rubinacci, Olivier Delaneau, Jonathan Marchini
bioRxiv 797944; doi: https://doi.org/10.1101/797944

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