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.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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).
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).
Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).
Cvejic, A. et al. SMIM1 underlies the Vel blood group and influences red blood cell traits. Nat. Genet. 45, 542–545 (2013).
Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).
Fu, J. et al. Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genet. 8, e1002431 (2012).
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).
Andiappan, A.K. et al. Genome-wide analysis of the genetic regulation of gene expression in human neutrophils. Nat. Commun. 6, 7971 (2015).
Francesconi, M. & Lehner, B. The effects of genetic variation on gene expression dynamics during development. Nature 505, 208–211 (2014).
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).
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).
Westra, H.-J. et al. Cell specific eQTL analysis without sorting cells. PLoS Genet. 11, e1005223 (2015).
Fairfax, B.P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).
Çalişkan, M., Baker, S.W., Gilad, Y. & Ober, C. Host genetic variation influences gene expression response to rhinovirus infection. PLoS Genet. 11, e1005111 (2015).
Lee, M.N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).
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).
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).
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).
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).
Hofman, A. et al. The Rotterdam Study: 2014 objectives and design update. Eur. J. Epidemiol. 28, 889–926 (2013).
Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).
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).
Ritchie, G.R.S., Dunham, I., Zeggini, E. & Flicek, P. Functional annotation of noncoding sequence variants. Nat. Methods 11, 294–296 (2014).
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
Naranbhai, V. et al. Genomic modulators of gene expression in human neutrophils. Nat. Commun. 6, 7545 (2015).
Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
Idaghdour, Y. et al. Geographical genomics of human leukocyte gene expression variation in southern Morocco. Nat. Genet. 42, 62–67 (2010).
Yao, C. et al. Sex- and age-interacting eQTLs in human complex diseases. Hum. Mol. Genet. 23, 1947–1956 (2014).
Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224–226 (2012).
Doré, L.C. & Crispino, J.D. Transcription factor networks in erythroid cell and megakaryocyte development. Blood 118, 231–239 (2011).
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).
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).
Rusinova, I. et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 41, D1040–D1046 (2013).
Heinrichs, S. et al. MYBL2 is a sub-haploinsufficient tumor suppressor gene in myeloid malignancy. eLife 2, e00825 (2013).
Platanias, L.C. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat. Rev. Immunol. 5, 375–386 (2005).
Ivashkiv, L.B. & Donlin, L.T. Regulation of type I interferon responses. Nat. Rev. Immunol. 14, 36–49 (2014).
McLeay, R.C. & Bailey, T.L. Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 11, 165 (2010).
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).
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).
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).
Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).
Dobin, A. et al. STAR: ultrafast universal RNA–seq aligner. Bioinformatics 29, 15–21 (2013).
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).
Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
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).
Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat. Genet. 46, 818–825 (2014).
Deelen, P. et al. Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration. BMC Res. Notes 7, 901 (2014).
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).
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).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Goya, R. et al. SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics 26, 730–736 (2010).
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).
Robinson, M.D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA–seq data. Genome Biol. 11, R25 (2010).
Fehrmann, R.S.N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).
Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Landt, S.G. et al. ChIP–seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).
Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).
Frey, B.J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).
Bodenhofer, U., Kothmeier, A. & Hochreiter, S. APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463–2464 (2011).
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.
The authors declare no competing financial interests.
Supplementary Figures 1–4, 7 and 9, Supplementary Tables 1–3 and 9–11, and Supplementary Note. (PDF 4801 kb)
GWAVA functional annotation of eSNPs. (XLSX 63 kb)
Enhancer enrichment. (XLSX 12 kb)
The set of trait/disease-associated variants used for eQTL annotation. (TXT 317 kb)
eQTLs associated with diseases and complex traits. (XLSX 547 kb)
Top 100 proxy genes and corresponding eQTLs for the top 10 interaction modules. (XLSX 5085 kb)
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)
Transcription factor enrichment analysis of significant context-specific eQTLs. (ZIP 533 kb)
GWAS hits for eQTLs significantly interacting with the top 10 modules. (XLSX 34 kb)
Interactions remaining after correcting for the first 10 proxy genes. (XLSX 320 kb)
Replication of interactions not falling into the top 10 modules in Geuvadis. (XLSX 14 kb)
Comparison of interaction score obtained by using cell counts and using proxy genes. (PDF 344 kb)
eQTLs associated with different autoimmune diseases. (PDF 2279 kb)
Plots of Picard metrics results and the outlier samples removed based on these metrics. (PDF 793 kb)
About this article
Cite this article
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
Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits
Genetics Selection Evolution (2021)
Genome-wide transcriptome study using deep RNA sequencing for myocardial infarction and coronary artery calcification
BMC Medical Genomics (2021)
Predictability of polygenic risk score for progression to dementia and its interaction with APOE ε4 in mild cognitive impairment
Translational Neurodegeneration (2021)
Correction for both common and rare cell types in blood is important to identify genes that correlate with age
BMC Genomics (2021)
Profile of copper-associated DNA methylation and its association with incident acute coronary syndrome
Clinical Epigenetics (2021)