Abstract

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology1,2. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations3,4,5,6. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease7,8,9,10,11,12,13,14,15,16,17. Here we generated peripheral blood RNA–seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

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Acknowledgements

This work was performed within the framework of the Biobank-Based Integrative Omics Studies (BIOS) consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007). Samples were contributed by LifeLines (http://lifelines.nl/lifelines-research/general), the Leiden Longevity Study (http://www.healthy-ageing.nl/ and http://www.leidenlangleven.nl/), the Rotterdam Studies (http://www.erasmus-epidemiology.nl/research/ergo.htm) and the CODAM study (http://www.carimmaastricht.nl/). We thank the participants of all aforementioned biobanks and acknowledge the contributions of the investigators to this study (Supplementary Note). This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative and the Groningen Center for Information Technology (G.J.C. Strikwerda, W. Albers, R. Teeninga, H. Gankema and H. Wind) and Target storage (E. Valentyn and R. Williams). Target is supported by Samenwerkingsverband Noord Nederland, the European Fund for Regional Development, the Dutch Ministry of Economic Affairs, Pieken in de Delta, and the provinces of Groningen and Drenthe. This work is supported by a grant from the European Research Council (ERC Starting Grant agreement 637640 ImmRisk) to L.F. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII) and the municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study are supported by the Netherlands Organization for Scientific Research NWO Investments (175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2) and Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) project 050-060-810. We thank P. Arp, M. Jhamai, M. Verkerk, L. Herrera and M. Peters for their help in creating the GWAS database. Work on cell count estimation was funded by NWO 863.13.011. The LifeLines Deep cohort is made possible by grants from the Top Institute of Food and Nutrition (TiFN GH0001), an ERC advanced grant (FP/2007-2013/ERC grant 2012-322698) and a Spinoza prize (NWO SPI 92-266) to C.W.

Author information

Author notes

    • Daria V Zhernakova
    • , Patrick Deelen
    • , Martijn Vermaat
    •  & Maarten van Iterson

    These authors contributed equally to this work.

    • Bastiaan T Heijmans
    • , Peter A C 't Hoen
    •  & Lude Franke

    These authors jointly directed this work.

Affiliations

  1. University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands.

    • Daria V Zhernakova
    • , Patrick Deelen
    • , Freerk van Dijk
    • , Marc Jan Bonder
    • , Alexandra Zhernakova
    • , Yang Li
    • , Ettje F Tigchelaar
    • , Niek de Klein
    • , Cisca Wijmenga
    • , Morris A Swertz
    •  & Lude Franke
  2. University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands.

    • Patrick Deelen
    • , Freerk van Dijk
    •  & Morris A Swertz
  3. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.

    • Martijn Vermaat
    • , Michiel van Galen
    •  & Peter A C 't Hoen
  4. Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands.

    • Maarten van Iterson
    • , Matthijs Moed
    • , Szymon M Kielbasa
    • , Marian Beekman
    • , Joris Deelen
    • , P Eline Slagboom
    •  & Bastiaan T Heijmans
  5. Sequence Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands.

    • Wibowo Arindrarto
    • , Peter van 't Hof
    •  & Hailiang Mei
  6. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Harm-Jan Westra
  7. Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA.

    • Harm-Jan Westra
  8. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Harm-Jan Westra
  9. Department of Internal Medicine, ErasmusMC, Rotterdam, the Netherlands.

    • Jeroen van Rooij
    • , Marijn Verkerk
    • , P Mila Jhamai
    • , André G Uitterlinden
    •  & Joyce B J van Meurs
  10. SURFsara, Amsterdam, the Netherlands.

    • Jan Bot
    •  & Irene Nooren
  11. Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands.

    • René Pool
    • , Jenny van Dongen
    • , Jouke J Hottenga
    •  & Dorret I Boomsma
  12. Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.

    • Coen D A Stehouwer
    • , Carla J H van der Kallen
    • , Casper G Schalkwijk
    •  & Marleen M J van Greevenbroek
  13. School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands.

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

    • Diana van Heemst
  15. Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.

    • Leonard H van den Berg
    •  & Jan H Veldink
  16. Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands.

    • Albert Hofman
  17. Genetic Epidemiology Unit, Department of Epidemiology, ErasmusMC, Rotterdam, the Netherlands.

    • Cornelia M van Duijn
    •  & Aaron Isaacs
  18. Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands.

    • Aaron Isaacs
  19. Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands.

    • Rick Jansen

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Contributions

B.T.H., P.A.C.'t.H., J.B.J.v.M., A.I., R.J. and L.F. formed the management team of the BIOS consortium. D.I.B., R.P., J.v.D., J.J.H., M.M.J.V.G., C.D.A.S., C.J.H.v.d.K., C.G.S., C.W., L.F., A.Z., E.F.T., P.E.S., M.B., J.D., D.v.H., J.H.V., L.H.v.d.B., C.M.v.D., A.H., A.I. and A.G.U. managed and organized the biobanks. J.B.J.v.M., P.M.J., M. Verkerk and J.v.R. generated RNA–seq data. H.M., M.v.I., M.v.G., W.A., J.B., D.V.Z., R.J., P.v.'t.H., P.D., M. Verkerk, M. Vermaat, I.N., M.A.S., P.A.C.'t.H., B.T.H. and M.M. were responsible for data management and the computational infrastructure. D.V.Z., P.D., M. Vermaat, M.v.I., F.v.D., M.v.G., W.A., M.J.B., N.d.K., H.-J.W., S.M.K., Y.L., M.A.S., P.A.C.'t.H. and L.F. performed the data analysis. D.V.Z., P.D., P.A.C.'t.H. and L.F. drafted the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Bastiaan T Heijmans or Peter A C 't Hoen or Lude Franke.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–4, 7 and 9, Supplementary Tables 1–3 and 9–11, and Supplementary Note.

  2. 2.

    Supplementary Figure 5

    Comparison of interaction score obtained by using cell counts and using proxy genes.

  3. 3.

    Supplementary Figure 6

    eQTLs associated with different autoimmune diseases.

  4. 4.

    Supplementary Figure 8

    Plots of Picard metrics results and the outlier samples removed based on these metrics.

Excel files

  1. 1.

    Supplementary Table 4

    GWAVA functional annotation of eSNPs.

  2. 2.

    Supplementary Table 5

    Enhancer enrichment.

  3. 3.

    Supplementary Table 7

    eQTLs associated with diseases and complex traits.

  4. 4.

    Supplementary Table 8

    Top 100 proxy genes and corresponding eQTLs for the top 10 interaction modules.

  5. 5.

    Supplementary Table 12

    Pathway enrichment analysis results for the cell-type-specific eQTL genes of the top 10 interaction modules for gene, exon and exon ratio level analysis.

  6. 6.

    Supplementary Table 14

    GWAS hits for eQTLs significantly interacting with the top 10 modules.

  7. 7.

    Supplementary Table 15

    Interactions remaining after correcting for the first 10 proxy genes.

  8. 8.

    Supplementary Table 16

    Replication of interactions not falling into the top 10 modules in Geuvadis.

Text files

  1. 1.

    Supplementary Table 6

    The set of trait/disease-associated variants used for eQTL annotation.

Zip files

  1. 1.

    Supplementary Table 13

    Transcription factor enrichment analysis of significant context-specific eQTLs.

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DOI

https://doi.org/10.1038/ng.3737

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