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


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.


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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 (, the Leiden Longevity Study ( and, the Rotterdam Studies ( and the CODAM study ( 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




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.

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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).

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