Abstract

Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

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Data availability

The BIOS RNA data can be obtained from the European Genome-phenome Archive (EGA; accession EGAS00001001077). Genotype data are available from the respective biobanks.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

The authors thank C. Lippert and L. Parts for helpful discussions. This research was conducted using the UK Biobank Resource (Application Number 14069). R.M. was supported by a PhD fellowship from the Mathematical Genomics and Medicine program, funded by the Wellcome Trust. F.P.C., D.H. and O.S. received support from core funding of the European Molecular Biology Laboratory and the European Union’s Horizon2020 research and innovation program under grant agreement N635290. I.B. acknowledges funding from Wellcome (WT098051 and WT206194). M.J.B. was supported by a fellowship from the EMBL Interdisciplinary Postdoc (EI3POD) program under Marie Skłodowska-Curie Actions COFUND (grant number 664726). The Biobank-Based Integrative Omics Studies (BIOS) Consortium is funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007).

Author information

Author notes

  1. These authors contributed equally: Rachel Moore, Francesco Paolo Casale.

Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    • Rachel Moore
    •  & Inês Barroso
  2. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    • Rachel Moore
    • , Marc Jan Bonder
    • , Danilo Horta
    •  & Oliver Stegle
  3. University of Cambridge, Cambridge, UK

    • Rachel Moore
  4. Microsoft Research New England, Cambridge, Massachusetts, USA

    • Francesco Paolo Casale
  5. University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands

    • Lude Franke
  6. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    • Oliver Stegle
  7. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    • Oliver Stegle
  8. Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands

    • Bastiaan T. Heijmans
    • , P. Eline Slagboom
    • , Marian Beekman
    • , Joris Deelen
    • , H. Eka D. Suchiman
    • , Ruud van der Breggen
    • , Nico Lakenberg
    • , Maarten van Iterson
    • , Matthijs Moed
    •  & René Luijk
  9. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

    • Peter A. C.’t Hoen
    • , Michael Verbiest
    • , Michiel van Galen
    •  & Martijn Vermaat
  10. Department of Internal Medicine, ErasmusMC, Rotterdam, the Netherlands

    • Joyce van Meurs
    • , André G. Uitterlinden
    • , P. Mila Jhamai
    • , Marijn Verkerk
    •  & Jeroen van Rooij
  11. Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands

    • Aaron Isaacs
    • , Jan H. Veldink
    •  & Leonard H. van den Berg
  12. Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands

    • Rick Jansen
  13. Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands

    • Lude Franke
    • , Cisca Wijmenga
    • , Alexandra Zhernakova
    • , Ettje F. Tigchelaar
    • , Patrick Deelen
    • , Dasha V. Zhernakova
    • , Marc Jan Bonder
    • , Freerk van Dijk
    •  & Morris A. Swertz
  14. Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands

    • Dorret I. Boomsma
    • , René Pool
    • , Jenny van Dongen
    •  & Jouke J. Hottenga
  15. Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands

    • Marleen M. J. van Greevenbroek
    • , Coen D. A. Stehouwer
    • , Carla J. H. van der Kallen
    •  & Casper G. Schalkwijk
  16. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands

    • Diana van Heemst
  17. Department of Genetic Epidemiology, ErasmusMC, Rotterdam, the Netherlands

    • Cornelia M. van Duijn
  18. Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands

    • Bert A. Hofman
  19. Sequence Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands

    • Hailiang Mei
    • , Peter van’t Hof
    •  & Szymon M. Kielbasa
  20. SURFsara, Amsterdam, the Netherlands

    • Jan Bot
    •  & Irene Nooren
  21. Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    • Freerk van Dijk
    •  & Morris A. Swertz
  22. Medical Statistics Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands

    • Wibowo Arindrarto
    •  & Erik W. van Zwet

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Consortia

  1. BIOS Consortium

Contributions

R.M., F.P.C., I.B., and O.S. conceived the method. R.M., F.P.C., and D.H. implemented the methods. R.M., F.P.C., and M.J.B. analyzed the data. L.F. and BIOS Consortium provided data resources. R.M., F.P.C., I.B., and O.S. interpreted results and wrote the paper.

Competing interests

F.P.C. was employed at Microsoft while performing the research.

Corresponding authors

Correspondence to Inês Barroso or Oliver Stegle.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–23, Supplementary Tables 1 and 2, and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 3

    Interactions identified by StructLMM for BMI in UK Biobank

  4. Supplementary Table 4

    Associations identified by StructLMM and LMM in the association analysis of BMI using data from UK Biobank

  5. Supplementary Table 5

    Summary table of interaction eQTL analysis in blood cohort

  6. Supplementary Table 6

    Pathway enrichment analysis for interaction eQTLs that are in linkage with GWAS loci

  7. Supplementary Data 1

    eQTL Manhattan plots for interaction eQTLs that colocalize with disease variants

  8. Supplementary Data 2

    Interaction eQTL colocalizing with disease variants

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DOI

https://doi.org/10.1038/s41588-018-0271-0