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.

  • Brief Communication
  • Published:

Allele-specific expression reveals interactions between genetic variation and environment

Abstract

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: EAGLE associates allele-specific expression (ASE) with environmental covariates to detect GxE interactions.
Figure 2: EAGLE detects GxE interactions missed by standard interaction QTL testing.
Figure 3: EAGLE detects allele-specific effects of direct perturbations and environments measured by 'proxy' genes.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

References

  1. Flint, J. & Mackay, T.F.C. Genome Res. 19, 723–733 (2009).

    Article  CAS  Google Scholar 

  2. Eichler, E.E. et al. Nat. Rev. Genet. 11, 446–450 (2010).

    Article  CAS  Google Scholar 

  3. Battle, A. et al. Genome Res. 24, 14–24 (2014).

    Article  CAS  Google Scholar 

  4. GTEx Consortium. Science 348, 648–660 (2015).

  5. Bray, M.S. et al. Am. J. Physiol. Heart Circ. Physiol. 294, H1036–H1047 (2008).

    Article  CAS  Google Scholar 

  6. Glass, D. et al. Genome Biol. 14, R75 (2013).

    Article  Google Scholar 

  7. Fairfax, B.P. et al. Science 343, 1246949 (2014).

    Article  Google Scholar 

  8. Lee, M.N. et al. Science 343, 1246980 (2014).

    Article  Google Scholar 

  9. Barreiro, L.B. et al. Proc. Natl. Acad. Sci. USA 109, 1204–1209 (2012).

    Article  CAS  Google Scholar 

  10. Brown, A.A. et al. eLife 3, e01381 (2014).

    Article  Google Scholar 

  11. Buil, A. et al. Nat. Genet. 47, 88–91 (2015).

    Article  CAS  Google Scholar 

  12. van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J.K. Nat. Methods 12, 1061–1063 (2015).

    Article  CAS  Google Scholar 

  13. Degner, J.F. et al. Bioinformatics 25, 3207–3212 (2009).

    Article  CAS  Google Scholar 

  14. Biondi, O. et al. Am. J. Pathol. 182, 2298–2309 (2013).

    Article  CAS  Google Scholar 

  15. Jacobo-Albavera, L. et al. PLoS One 7, e49818 (2012).

    Article  CAS  Google Scholar 

  16. Schadt, E.E. et al. Nat. Genet. 37, 710–717 (2005).

    Article  CAS  Google Scholar 

  17. Bu˚žková, P., Lumley, T. & Rice, K. Ann. Hum. Genet. 75, 36–45 (2011).

    Article  Google Scholar 

  18. Hussin, J.G. et al. Nat. Genet. 47, 400–404 (2015).

    Article  CAS  Google Scholar 

  19. Hodgkinson, A. et al. Science 344, 413–415 (2014).

    Article  CAS  Google Scholar 

  20. Burgess, J.L. et al. J. Occup. Environ. Med. 46, 1013–1022 (2004).

    Article  CAS  Google Scholar 

  21. Pejnovic, N.N. et al. Diabetes 62, 1932–1944 (2013).

    Article  CAS  Google Scholar 

  22. Wang, C. et al. Nat. Biotechnol. 32, 926–932 (2014).

    Article  CAS  Google Scholar 

  23. Kersten, S. Mol. Metab. 3, 354–371 (2014).

    Article  CAS  Google Scholar 

  24. Mostafavi, S. et al. PLoS One 8, e68141 (2013).

  25. Panousis, N.I., Gutierrez-Arcelus, M., Dermitzakis, E.T. & Lappalainen, T. Genome Biol. 15, 467 (2014).

    Article  Google Scholar 

  26. Kim, D. et al. Genome Biol. 14, R36 (2013).

    Article  Google Scholar 

  27. Li, H. et al. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  28. Harvey, C.T. et al. Bioinformatics 31, 1235–1242 (2015).

    Article  Google Scholar 

  29. Castel, S.E., Levy-Moonshine, A., Mohammadi, P., Banks, E. & Lappalainen, T. Genome Biol. 16, 195 (2015).

    Article  Google Scholar 

  30. Kumasaka, N., Knights, A.J. & Gaffney, D.J. Nat. Genet. 48, 206–213 (2016).

    Article  CAS  Google Scholar 

  31. Knowles, D.A. & Minka, T. Adv. Neural Inf. Process. Syst. 24, 1701–1709 (2011).

    Google Scholar 

Download references

Acknowledgements

We would like to thank J. Leek for helpful comments and S. Kersten for providing the graphic from which the PPARα network figure was adapted. D.A.K. is supported by NIH U54CA149145. M.-J.F. is supported by a CIHR Neuroinflammation fellowship. P.A. is supported by the Ontario Ministry of Research and Innovation. A.B. and S.B.M. are supported by NIH R01MH101814 and NIH R01HG008150. A.B. is supported by the Searle Scholars Program, NIH R01MH101820, NIH 1R01MH109905-01, and NIH 1R01GM120167-010. S.B.M. is supported by the Edward Mallinckrodt Jr. Foundation.

Author information

Authors and Affiliations

Authors

Contributions

D.A.K., S.B.M. and A.B. conceived the project and wrote the manuscript. D.A.K. and A.B. developed the method. D.A.K. implemented the software and performed the main analyses. J.R.D. and A.R. performed additional statistical analyses. X.Z., J.B.P., M.M.W., J.S., S.M. and D.F.L. gave input regarding the DGN cohort. Supervised by P.A. and M.-J.F., H.E. ran EAGLE on the CARTaGENE replication cohort. S.B.M. and A.B. supervised the project.

Corresponding authors

Correspondence to Stephen B Montgomery or Alexis Battle.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Tables 1–4 and Supplementary Notes 1–11. (PDF 3649 kb)

Supplementary Software

Source code for EAGLE software. (ZIP 36 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Knowles, D., Davis, J., Edgington, H. et al. Allele-specific expression reveals interactions between genetic variation and environment. Nat Methods 14, 699–702 (2017). https://doi.org/10.1038/nmeth.4298

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4298

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing