Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Rapid genotype imputation from sequence without reference panels

Nature Genetics volume 48, pages 965–969 (2016)Cite this article

Subjects

Abstract

Inexpensive genotyping methods are essential for genetic studies requiring large sample sizes. In human studies, array-based microarrays and high-density haplotype reference panels allow efficient genotype imputation for this purpose. However, these resources are typically unavailable in non-human settings. Here we describe a method (STITCH) for imputation based only on sequencing read data, without requiring additional reference panels or array data. We demonstrate its applicability even in settings of extremely low sequencing coverage, by accurately imputing 5.7 million SNPs at a mean r2 value of 0.98 in 2,073 outbred laboratory mice (0.15× sequencing coverage). In a sample of 11,670 Han Chinese (1.7× coverage), we achieve accuracy similar to that of alternative approaches that require a reference panel, demonstrating that our approach can work for genetically diverse populations. Our method enables straightforward progression from low-coverage sequence to imputed genotypes, overcoming barriers that at present restrict the application of genome-wide association study technology outside humans.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Overview of STITCH.
Figure 2: Performance of STITCH on CFW mice in comparison to external validation.
Figure 3: Performance of STITCH on CONVERGE humans in comparison to external validation.
Figure 4: Effects of reduced sequence coverage.

References

  1. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP–trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  Google Scholar 

  2. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

  3. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  4. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  Google Scholar 

  5. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  6. Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  Google Scholar 

  7. Browning, S.R. & Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  Google Scholar 

  8. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    Article  CAS  Google Scholar 

  9. Swarts, K. et al. Novel methods to optimize genotypic imputation for low-coverage, next-generation sequence data in crop plants. Plant Genome http://dx.doi.org/10.3835/plantgenome2014.05.0023 (2014).

  10. Huang, B.E. & George, A.W. R/mpMap: a computational platform for the genetic analysis of multiparent recombinant inbred lines. Bioinformatics 27, 727–729 (2011).

    Article  CAS  Google Scholar 

  11. Sargolzaei, M., Chesnais, J.P. & Schenkel, F.S. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478 (2014).

    Article  Google Scholar 

  12. VanRaden, P.M., Sun, C. & O'Connell, J.R. Fast imputation using medium or low-coverage sequence data. BMC Genet. 16, 82 (2015).

    Article  Google Scholar 

  13. Didion, J.P. et al. Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC Genomics 13, 34 (2012).

    Article  CAS  Google Scholar 

  14. Pasaniuc, B. et al. Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat. Genet. 44, 631–635 (2012).

    Article  CAS  Google Scholar 

  15. CONVERGE Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).

  16. Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644 (2006).

    Article  CAS  Google Scholar 

  17. Nicod, J. et al. Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing. Nat. Genet. http://dx.doi.org/10.1038/ng.3595 (2016).

  18. Yalcin, B. et al. Commercially available outbred mice for genome-wide association studies. PLoS Genet. 6, e1001085 (2010).

    Article  Google Scholar 

  19. Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    Article  CAS  Google Scholar 

  20. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  Google Scholar 

  21. Freedman, A.H. et al. Genome sequencing highlights the dynamic early history of dogs. PLoS Genet. 10, e1004016 (2014).

    Article  Google Scholar 

  22. Bovine HapMap Consortium. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324, 528–532 (2009).

  23. Daetwyler, H.D. et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat. Genet. 46, 858–865 (2014).

    Article  CAS  Google Scholar 

  24. VanBuren, R. et al. Single-molecule sequencing of the desiccation-tolerant grass Oropetium thomaeum. Nature 527, 508–511 (2015).

    Article  CAS  Google Scholar 

  25. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  26. Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).

    Article  CAS  Google Scholar 

  27. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

R.W.D. is supported by a grant from the Wellcome Trust (097308/Z/11/Z). S.M. is supported by Investigator Award 098387/Z/12/Z. This work was funded by the Wellcome Trust (WT090532/Z/09/Z, WT083573/Z/07/Z, WT089269/Z/09/Z and WT098387/Z/12/Z).

Author information

Author notes

  1. Simon Myers and Richard Mott: These authors contributed equally to this work.

Authors and Affiliations

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

    Robert W Davies, Simon Myers & Richard Mott

  2. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California, USA

    Jonathan Flint

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

    Simon Myers

  4. UCL Genetics Institute, University College London, London, UK

    Richard Mott

Authors
  1. Robert W Davies
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan Flint
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Myers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard Mott
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.W.D., S.M., and R.M. developed the method. R.W.D. wrote the algorithm and performed analyses. J.F. and R.M. conceived and managed the CFW and CONVERGE projects. All authors contributed to study design, drafted the paper, and reviewed and contributed to the final manuscript.

Corresponding author

Correspondence to Robert W Davies.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–8 and Supplementary Note. (PDF 1385 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Davies, R., Flint, J., Myers, S. et al. Rapid genotype imputation from sequence without reference panels. Nat Genet 48, 965–969 (2016). https://doi.org/10.1038/ng.3594

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3594

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research