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Next-generation genotype imputation service and methods

Nature Genetics volume 48, pages 1284–1287 (2016)Cite this article

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Abstract

Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.

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Figure 1: Overview of state space reduction.

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Acknowledgements

The authors gratefully acknowledge D. Hinds for assistance with minimac3 code optimizations and A.L. Williams for providing HAPI-UR. We acknowledge support from National Institutes of Health grants HG007022 and HL117626 (G.R.A.), HG000376 (M.B.), and R01DA037904 (S.I.V.), Austrian Science Fund (FWF) grant J-3401 (C.F.), and the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement 602133 (L.F. and S.S.). This work was also supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health (D. Schlessinger).

Author information

Author notes

  1. Sayantan Das, Lukas Forer and Sebastian Schönherr: These authors contributed equally to this work.

  2. Gonçalo R Abecasis and Christian Fuchsberger: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Sayantan Das, Carlo Sidore, Adam E Locke, Alan Kwong, Laura J Scott, Michael Boehnke, Gonçalo R Abecasis & Christian Fuchsberger

  2. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria

    Lukas Forer, Sebastian Schönherr, Florian Kronenberg & Christian Fuchsberger

  3. Istituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Cagliari, Italy

    Carlo Sidore & Francesco Cucca

  4. Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy

    Carlo Sidore & Francesco Cucca

  5. Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA

    Scott I Vrieze

  6. Division of Epidemiology and Clinical Applications, Clinical Trials Branch, National Eye Institute, US National Institutes of Health, Bethesda, Maryland, USA

    Emily Y Chew

  7. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA

    Shawn Levy

  8. Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA

    Matt McGue & William G Iacono

  9. Laboratory of Genetics, National Institute on Aging, US National Institutes of Health, Baltimore, Maryland, USA

    David Schlessinger

  10. Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Dwight Stambolian

  11. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Po-Ru Loh

  12. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Po-Ru Loh

  13. Neurobiology–Neurodegeneration and Repair Laboratory, National Eye Institute, US National Institutes of Health, Bethesda, Maryland, USA

    Anand Swaroop

  14. Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), affiliated with the University of Lübeck, Bolzano, Italy

    Christian Fuchsberger

Authors
  1. Sayantan Das
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  2. Lukas Forer
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  3. Sebastian Schönherr
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  4. Carlo Sidore
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  5. Adam E Locke
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  6. Alan Kwong
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  7. Scott I Vrieze
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  8. Emily Y Chew
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  9. Shawn Levy
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  10. Matt McGue
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  11. David Schlessinger
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  14. William G Iacono
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  15. Anand Swaroop
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  16. Laura J Scott
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  17. Francesco Cucca
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  18. Florian Kronenberg
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  19. Michael Boehnke
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  20. Gonçalo R Abecasis
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  21. Christian Fuchsberger
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Contributions

S.D., L.F., S.S., G.R.A., and C.F. designed the methods and experiments. C.S., A.E.L., A.K., S.I.V., E.Y.C., S.L., M.M., D. Schlessinger, P.-R.L., D. Stambolian, W.G.I., A.S., L.J.S., F.C., F.K., and M.B. provided data or tools. S.D., G.R.A., and C.F. wrote the first draft. All authors contributed critical reviews of the manuscript during its preparation.

Corresponding authors

Correspondence to Gonçalo R Abecasis or Christian Fuchsberger.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Imputation server overview.

The imputation workflow uses two MapReduce jobs to parallelize the quality control and the phasing/imputation step.

Supplementary Figure 2 Quality control workflow for each variant site.

Supplementary Figure 3 Parameter estimation study.

The figure compares the imputation accuracy across three parameter estimation methods on six different populations from the Human Genome Diversity Project (HGDP) on chromosomes 20–22.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Tables 1–4 and Supplementary Note. (PDF 2575 kb)

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Das, S., Forer, L., Schönherr, S. et al. Next-generation genotype imputation service and methods. Nat Genet 48, 1284–1287 (2016). https://doi.org/10.1038/ng.3656

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