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Identification of context-dependent expression quantitative trait loci in whole blood

Nature Genetics volume 49, pages 139–145 (2017)Cite this article

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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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Figure 1: Over 20,000 genes are regulated by cis-eQTLs overlapping with 33% of the entries in the NHGRI GWAS catalog.
Figure 2: Identification of the strongest modifiers of eQTL effects.
Figure 3: eQTLs modified by type I interferon signaling.
Figure 4: FADS2 eQTL modulated by SREBF2 expression.
Figure 5: A MYBL2 eQTL is modulated by the B cell proliferation gene EBF1.

References

  1. Schaub, M.A., Boyle, A.P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Cvejic, A. et al. SMIM1 underlies the Vel blood group and influences red blood cell traits. Nat. Genet. 45, 542–545 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Fu, J. et al. Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genet. 8, e1002431 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fairfax, B.P. et al. Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Andiappan, A.K. et al. Genome-wide analysis of the genetic regulation of gene expression in human neutrophils. Nat. Commun. 6, 7971 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Francesconi, M. & Lehner, B. The effects of genetic variation on gene expression dynamics during development. Nature 505, 208–211 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Powell, J.E. et al. Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent. Genome Res. 22, 456–466 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Deelen, P. et al. Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels. Genome Med. 7, 30 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Westra, H.-J. et al. Cell specific eQTL analysis without sorting cells. PLoS Genet. 11, e1005223 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fairfax, B.P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Çalişkan, M., Baker, S.W., Gilad, Y. & Ober, C. Host genetic variation influences gene expression response to rhinovirus infection. PLoS Genet. 11, e1005111 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lee, M.N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Barreiro, L.B. et al. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. Proc. Natl. Acad. Sci. USA 109, 1204–1209 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. van Greevenbroek, M.M.J. et al. The cross-sectional association between insulin resistance and circulating complement C3 is partly explained by plasma alanine aminotransferase, independent of central obesity and general inflammation (the CODAM study). Eur. J. Clin. Invest. 41, 372–379 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Tigchelaar, E.F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Schoenmaker, M. et al. Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study. Eur. J. Hum. Genet. 14, 79–84 (2006).

    Article  PubMed  Google Scholar 

  21. Hofman, A. et al. The Rotterdam Study: 2014 objectives and design update. Eur. J. Epidemiol. 28, 889–926 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wood, A.R. et al. Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association. Hum. Mol. Genet. 20, 4082–4092 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ritchie, G.R.S., Dunham, I., Zeggini, E. & Flicek, P. Functional annotation of noncoding sequence variants. Nat. Methods 11, 294–296 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Naranbhai, V. et al. Genomic modulators of gene expression in human neutrophils. Nat. Commun. 6, 7545 (2015).

    Article  PubMed  Google Scholar 

  28. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Idaghdour, Y. et al. Geographical genomics of human leukocyte gene expression variation in southern Morocco. Nat. Genet. 42, 62–67 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Yao, C. et al. Sex- and age-interacting eQTLs in human complex diseases. Hum. Mol. Genet. 23, 1947–1956 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224–226 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Doré, L.C. & Crispino, J.D. Transcription factor networks in erythroid cell and megakaryocyte development. Blood 118, 231–239 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Hall, M.A. et al. The critical regulator of embryonic hematopoiesis, SCL, is vital in the adult for megakaryopoiesis, erythropoiesis, and lineage choice in CFU-S12. Proc. Natl. Acad. Sci. USA 100, 992–997 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pevny, L. et al. Erythroid differentiation in chimaeric mice blocked by a targeted mutation in the gene for transcription factor GATA-1. Nature 349, 257–260 (1991).

    Article  CAS  PubMed  Google Scholar 

  35. Rusinova, I. et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 41, D1040–D1046 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Heinrichs, S. et al. MYBL2 is a sub-haploinsufficient tumor suppressor gene in myeloid malignancy. eLife 2, e00825 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Platanias, L.C. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat. Rev. Immunol. 5, 375–386 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Ivashkiv, L.B. & Donlin, L.T. Regulation of type I interferon responses. Nat. Rev. Immunol. 14, 36–49 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. McLeay, R.C. & Bailey, T.L. Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 11, 165 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Facchetti, F., Cella, M., Festa, S., Fremont, D.H. & Colonna, M. An unusual Fc receptor–related protein expressed in human centroblasts. Proc. Natl. Acad. Sci. USA 99, 3776–3781 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rosén, A. et al. Lymphoblastoid cell line with B1 cell characteristics established from a chronic lymphocytic leukemia clone by in vitro EBV infection. OncoImmunology 1, 18–27 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  42. van Dam, R.M., Boer, J.M., Feskens, E.J.M. & Seidell, J.C. Parental history of diabetes modifies the association between abdominal adiposity and hyperglycemia. Diabetes Care 24, 1454–1459 (2001).

    Article  CAS  PubMed  Google Scholar 

  43. Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).

    Article  PubMed  Google Scholar 

  44. Dobin, A. et al. STAR: ultrafast universal RNA–seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Liu, Z. et al. Comparing computational methods for identification of allele-specific expression based on next generation sequencing data. Genet. Epidemiol. 38, 591–598 (2014).

    Article  PubMed  Google Scholar 

  46. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Deelen, J. et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Hum. Mol. Genet. 23, 4420–4432 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat. Genet. 46, 818–825 (2014).

  49. Deelen, P. et al. Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration. BMC Res. Notes 7, 901 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 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  PubMed  PubMed Central  Google Scholar 

  51. Deelen, P. et al. Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of The Netherlands'. Eur. J. Hum. Genet. 22, 1321–1326 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Goya, R. et al. SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics 26, 730–736 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Westra, H.-J. et al. MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics 27, 2104–2111 (2011).

    Article  CAS  PubMed  Google Scholar 

  55. Robinson, M.D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA–seq data. Genome Biol. 11, R25 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Fehrmann, R.S.N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Landt, S.G. et al. ChIP–seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Frey, B.J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Bodenhofer, U., Kothmeier, A. & Hochreiter, S. APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463–2464 (2011).

    Article  CAS  PubMed  Google Scholar 

Download references

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

  1. Daria V Zhernakova, Patrick Deelen, Martijn Vermaat and Maarten van Iterson: These authors contributed equally to this work.

  2. Bastiaan T Heijmans, Peter A C 't Hoen and Lude Franke: These authors jointly directed this work.

Authors and 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. Department of Genetics, University of Groningen, University Medical Center Groningen, 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. Department of Medical Statistics and Bioinformatics, Molecular Epidemiology Section, 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. Department of Epidemiology, Genetic Epidemiology Unit, 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

Authors
  1. Daria V Zhernakova
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  2. Patrick Deelen
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  3. Martijn Vermaat
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  4. Maarten van Iterson
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  5. Michiel van Galen
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  6. Wibowo Arindrarto
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  7. Peter van 't Hof
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  8. Hailiang Mei
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  9. Freerk van Dijk
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  10. Harm-Jan Westra
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  11. Marc Jan Bonder
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  12. Jeroen van Rooij
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  13. Marijn Verkerk
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  14. P Mila Jhamai
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  15. Matthijs Moed
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  16. Szymon M Kielbasa
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  17. Jan Bot
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  18. Irene Nooren
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  19. René Pool
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  20. Jenny van Dongen
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  21. Jouke J Hottenga
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  22. Coen D A Stehouwer
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  23. Carla J H van der Kallen
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  24. Casper G Schalkwijk
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  25. Alexandra Zhernakova
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  26. Yang Li
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  27. Ettje F Tigchelaar
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  28. Niek de Klein
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  29. Marian Beekman
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  30. Joris Deelen
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  31. Diana van Heemst
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  32. Leonard H van den Berg
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  33. Albert Hofman
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  34. André G Uitterlinden
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  35. Marleen M J van Greevenbroek
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  36. Jan H Veldink
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  37. Dorret I Boomsma
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  38. Cornelia M van Duijn
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  39. Cisca Wijmenga
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  40. P Eline Slagboom
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  41. Morris A Swertz
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  42. Aaron Isaacs
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  43. Joyce B J van Meurs
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  44. Rick Jansen
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  45. Bastiaan T Heijmans
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  46. Peter A C 't Hoen
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  47. Lude Franke
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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.

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, 7 and 9, Supplementary Tables 1–3 and 9–11, and Supplementary Note. (PDF 4801 kb)

Supplementary Table 4

GWAVA functional annotation of eSNPs. (XLSX 63 kb)

Supplementary Table 5

Enhancer enrichment. (XLSX 12 kb)

Supplementary Table 6

The set of trait/disease-associated variants used for eQTL annotation. (TXT 317 kb)

Supplementary Table 7

eQTLs associated with diseases and complex traits. (XLSX 547 kb)

Supplementary Table 8

Top 100 proxy genes and corresponding eQTLs for the top 10 interaction modules. (XLSX 5085 kb)

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. (XLSX 147 kb)

Supplementary Table 13

Transcription factor enrichment analysis of significant context-specific eQTLs. (ZIP 533 kb)

Supplementary Table 14

GWAS hits for eQTLs significantly interacting with the top 10 modules. (XLSX 34 kb)

Supplementary Table 15

Interactions remaining after correcting for the first 10 proxy genes. (XLSX 320 kb)

Supplementary Table 16

Replication of interactions not falling into the top 10 modules in Geuvadis. (XLSX 14 kb)

Supplementary Figure 5

Comparison of interaction score obtained by using cell counts and using proxy genes. (PDF 344 kb)

Supplementary Figure 6

eQTLs associated with different autoimmune diseases. (PDF 2279 kb)

Supplementary Figure 8

Plots of Picard metrics results and the outlier samples removed based on these metrics. (PDF 793 kb)

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Zhernakova, D., Deelen, P., Vermaat, M. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet 49, 139–145 (2017). https://doi.org/10.1038/ng.3737

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