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Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

Nature Genetics volume 44, pages 955–959 (2012)Cite this article

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Abstract

The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs. We first statistically estimate the haplotypes for each individual within the GWAS sample (pre-phasing) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because (i) the GWAS samples must be phased only once, whereas standard methods would implicitly repeat phasing with each reference panel update, and (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match two unphased GWAS genotypes to a pair of reference haplotypes. We implemented our approach in the MaCH and IMPUTE2 frameworks, and we tested it on data sets from the Wellcome Trust Case Control Consortium 2 (WTCCC2), the Genetic Association Information Network (GAIN), the Women's Health Initiative (WHI) and the 1000 Genomes Project. This strategy will be particularly valuable for repeated imputation as reference panels evolve.

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Figure 1: Imputation schematic.

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Acknowledgements

We thank M. Boehnke for critical reading, advice and suggestion, Y. Li for aid with cleaning the WHI data and the two anonymous reviewers for their helpful comments. B.H. and M.S. were supported by a grant from the National Human Genome Research Institute (NHGRI; HGO2585) to M.S. J.M. was supported by a grant from the UK Medical Research Council (G0801823). C.F. and G.R.A. were supported by grants from the US National Institutes of Health (NIH; DK0855840, HG005552 and HG005581). This study makes use of data generated by the WTCCC, GAIN and WHI. A full list of the investigators who contributed to the generation of the WTCCC data is available from the WTCCC web site (see URLs). The WTCCC was partially funded by the Wellcome Trust under awards 076113 and 085475. For details of contributors to the GAIN and WHI studies, please see the corresponding dbGaP accessions.

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Author notes

  1. Bryan Howie and Christian Fuchsberger: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA

    Bryan Howie & Matthew Stephens

  2. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA

    Christian Fuchsberger & Gonçalo R Abecasis

  3. Department of Statistics, University of Chicago, Chicago, Illinois, USA

    Matthew Stephens

  4. Department of Statistics, University of Oxford, Oxford, UK

    Jonathan Marchini

  5. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Jonathan Marchini

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  1. Bryan Howie
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  3. Matthew Stephens
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Contributions

B.H., C.F., M.S., J.M. and G.R.A. designed the methods and experiments. B.H. and C.F. ran the experiments and wrote the first draft; all authors contributed critical reviews of the manuscript during its preparation.

Corresponding authors

Correspondence to Matthew Stephens, Jonathan Marchini or Gonçalo R Abecasis.

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The authors declare no competing financial interests.

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Supplementary Note, Supplementary Table 1 and Supplementary Figures 1–4 (PDF 465 kb)

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Howie, B., Fuchsberger, C., Stephens, M. et al. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44, 955–959 (2012). https://doi.org/10.1038/ng.2354

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