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    Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

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    Author
    Huang, J; Howie, B; McCarthy, S; Memari, Y; Walter, K; Min, JL; Danecek, P; Malerba, G; Trabetti, E; Zheng, H-F; ...
    Date
    2015-09-01
    Source Title
    Nature Communications
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    Kaye, Jane
    Affiliation
    Melbourne Law School
    Metadata
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    Document Type
    Journal Article
    Citations
    Huang, J., Howie, B., McCarthy, S., Memari, Y., Walter, K., Min, J. L., Danecek, P., Malerba, G., Trabetti, E., Zheng, H. -F., Gambaro, G., Richards, J. B., Durbin, R., Timpson, N. J., Marchini, J. & Soranzo, N. (2015). Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. NATURE COMMUNICATIONS, 6 (1), https://doi.org/10.1038/ncomms9111.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256997
    DOI
    10.1038/ncomms9111
    Abstract
    Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants.

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