Abstract
Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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Data availability
Primary genotype and gene expression data were analyzed by individual cohorts participating in the study, and our study analyzed summary statistics. Full summary statistics of eQTLGen cis-eQTL, trans-eQTL and eQTS meta-analyses are available on the eQTLGen website, http://www.eqtlgen.org, which was built using the MOLGENIS framework76. We also provide cis-eQTL files formatted for use in SMR, MAFs and replication statistics for cis-eQTL, trans-eQTL and eQTSs. Per-cohort summary statistics for discovery cohorts can be made available after approval of an analysis proposal in eQTLGen and with agreement of the cohort PIs; contact corresponding authors for further information. Trait-associated variants were collected from the EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/, accessed on 21 November 2016), the NIH GWAS Catalog (now hosted by the EBI GWAS Catalog, https://www.ebi.ac.uk/gwas/) and Immunobase (http://www.immunobase.org, accessed 26 April 2016; now hosted by Open Targets at https://genetics.opentargets.org/immunobase). Sources of numerous GWAS summary statistics used for eQTS analyses are outlined in the Supplementary Note and Supplementary Table 13. ExAC pLI scores used for Fig. 2 originate from ftp://ftp.broadinstitute.org/pub/ExAC_release/release0.3.1/functional_gene_constraint/fordist_cleaned_exac_r03_march16_z_pli_rec_null_data.txt. Genotype reference files used for harmonizing discovery datasets for meta-analysis originate from ftp://share.sph.umich.edu/1000genomes/fullProject/2012.03.14/GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz. The gene model used for gene annotations originates from Ensembl version 71 (ftp://ftp.ensembl.org/pub/release-71/gtf/homo_sapiens/Homo_sapiens.GRCh37.71.gtf.gz). FANTOM TF annotations used for eQTS enrichment analyses originate from http://fantom.gsc.riken.jp/5/sstar/Browse_Transcription_Factors_hg19. ChIP-seq data used for cis-eQTL overlap originate from https://www.chicp.org/. PPI data used for trans-eQTL mechanism enrichment analyses originate from https://www.intomics.com/inbio/map/api/get_data?file=InBio_Map_core_2016_09_12.tar.gz. Hi-C data used for trans-eQTL mechanism enrichment are deposited in the GEO (GM12878, GEO accession GSE63525). Curated gene sets used for enrichment analyses (gene ontology sets, ENCODE ChIP-X and CheA ChIP-X TF targets, TRANSFAC and JASPAR PWMs, ARCHS4 tissue expression, TargetScan miRNA target predictions, TarBase miRNA validated targets) were downloaded from the Enrichr website (https://maayanlab.cloud/Enrichr/#stats). Gene expression summaries and metadata from GTEx version 7 originate from https://gtexportal.org/home/. Gene expression summaries from BIOS are available in the BIOS Omics Atlas (http://bbmri.researchlumc.nl/atlas/#data). Per-cohort individual-level genotype and gene expression data are governed by respective biobanks and access can be requested according to procedures established by each biobank, with relevant restrictions applying as imposed by the IRB or local legislation. Data-access procedures established for the BIOS Consortium are available at https://www.bbmri.nl/acquisition-use-analyze/bios. Source data are provided with this paper.
Code availability
Individual cohorts participating in the study followed analysis plans as specified in our analysis cookbooks (https://github.com/molgenis/systemsgenetics/wiki/eQTL-mapping-analysis-cookbook-(eQTLGen), https://github.com/molgenis/systemsgenetics/wiki/eQTL-mapping-analysis-cookbook-for-RNA-seq-data, https://github.com/molgenis/systemsgenetics/wiki/QTL-mapping-analysis-cookbook-for-Affymetrix-expression-arrays) or with slight alterations as described in the Methods and the Supplementary Note. Tools and source codes used for genotype harmonization, identification of sample mix-ups, eQTL mapping, meta-analyses and calculation of PGSs are available at https://github.com/molgenis/systemsgenetics/. Tools used for primary analyses were written in Java (versions 6–8, https://www.java.com/). PLINK version 1.0.7 (https://zzz.bwh.harvard.edu/plink/) and version 1.90 (https://www.cog-genomics.org/plink/1.9/) was used for clumping and pruning. Downstream analyses and plots were performed and constructed with R (versions 3.4.4, 3.6.1 and 4.0.0, https://cran.r-project.org/) using packages data.table version 1.12 (https://cran.r-project.org/web/packages/data.table/), tidyverse version 1.2.1 (https://cran.r-project.org/web/packages/tidyverse/), broom version 0.5.1 (https://cran.r-project.org/web/packages/broom/), pheatmap version 1.0.12 (https://cran.r-project.org/web/packages/pheatmap/) and GeneOverlap version 1.18.0 (https://bioconductor.org/packages/release/bioc/html/GeneOverlap.html). Power analyses were conducted with the R package pwr version 1.3-0 (https://cran.r-project.org/web/packages/pwr/). scRNA-seq analyses were performed using the Cell Ranger Single Cell Software Suite version 3.0.2 (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) and its implementation of STAR aligner. The ToppGene web tool (https://toppgene.cchmc.org/) was used for some interpretative enrichment analyses, as well as the GeneNetwork web tool (https://genenetwork.nl/). The Decon2 framework (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) was used for predicting cell counts in BIOS data. We formatted our cis-eQTL into the BESD format using SMR (https://cnsgenomics.com/software/smr/#Overview).
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Acknowledgements
The cohorts participating in this study list their acknowledgements in the cohort-specific sections of the Supplementary Note. This work is supported by a grant from the European Research Council (ERC, ERC Starting Grant agreement number 637640 ImmRisk), a VIDI grant (917.14.374) and a VICI grant from the Netherlands Organisation for Scientific Research (NWO) to L.F. This work has been supported by the European Regional Development Fund and the program Mobilitas Pluss (MOBTP108) to U.Võsa. The project was supported by the ‘De Drie Lichten’ foundation in the Netherlands with a grant to A.C. M.G.N. is supported by ZonMw grants 849200011 and 531003014 from the Netherlands Organisation for Health Research and Development, a VENI grant from the NWO (VI.Veni.191G.030) and a Jacobs Foundation research fellowship. H.Y. is funded by a Diabetes UK RD Lawrence fellowship (17/0005594). This project received funding from the ERC under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 772376 (EScORIAL)) to J.H.V. T.E. and A.K. were supported by the Estonian Research Council grant PRG (PRG1291). A.Battle was supported by NIH grant R01MH109905, NIH grant R01HG008150 (NHGRI; Non-Coding Variants Program) and NIH grant R01MH101814 (NIH Common Fund; GTEx Program). M.G.P.v.d.W. was funded by the Nederlandse Organisatie voor Wetenschappelijk onderzoek, NWO-Veni 192.029. This work was supported by NIH grants R21ES024834 (B.Pierce), R01ES020506 (B.Pierce), R01ES023834 (B.Pierce), R35ES028379 (B.Pierce) and R01CA107431 (H.A.). This work was supported by the Sigrid Juselius Foundation (J.Kettunen) and funds from the Academy of Finland (grant numbers 297338 and 307247) (J.Kettunen) and the Novo Nordisk Foundation (grant number NNF17OC0026062) (J.Kettunen). S.Ripatti was supported by the Academy of Finland Centre of Excellence in Complex Disease Genetics (grant no. 312062). M.G. was supported by EU Horizon 2020 (grant 733100 for SYSCID) and a grant from the Excellence of Science (FNRS and FWO) (grant no. 30770923). We acknowledge support from the BBMRI-NL (Biobanking and Biomolecular Resources Research Infrastructure 184.021.007 and 184.033.111), Spinozapremie (NWO 56-464-14192), the ERC (ERC Advanced 230374) and the KNAW Academy Professor Award (PAH/6635) to D.I.B. G.H. works in a unit that receives funding from the UK MRC (MC_UU_12013/1&2&5) and the University of Bristol. S.B. was supported by the Swiss National Science Foundation (310030-152724). B.M.P. was supported by CHARGE infrastructure grant number HJ105756 for the HVH cohort. This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (grant 01ZX1906B) and by LIFE (Leipzig Research Center for Civilization Diseases), Universität Leipzig (which is funded by the European Union, by the European Regional Development Fund and by the Free State of Saxony within the framework of the excellence initiative to H.K. and M.Scholz). We thank the UMCG Genomics Coordination Center, the MOLGENIS team, the UG Center for Information Technology and the UMCG research IT program and their sponsors, in particular the BBMRI-NL for data storage, high-performance computing and web hosting infrastructure. The BBMRI-NL is a research infrastructure financed by the NWO (grant number 184.033.111). We thank K. McIntyre for editing the manuscript text.
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Contributions
U.Võsa. and A.C. coordinated consortium analyses, ran meta-analyses, interpreted data, performed downstream analyses and drafted and revised the manuscript. H.-J.W., M.J.B. and P.D. developed software used in the analyses, performed downstream analyses and participated in manuscript writing and revisions. L.F. and T.E. conceived the study. L.F. supervised the project, ran downstream analyses and participated in manuscript writing and revisions. B.Z., H.K., A.S., S.K., N.P., I.A., M.-J.F., M.A., M.W.C., R.J., I.S., L.T., A.Teumer., K.S., J.V., H.Y., V.K., A.K., J. Kettunen, J.P. and B.L. ran consortium analyses in their respective cohorts. A.S., R.K., S.K., G.H., R.S. and A.Brown ran replication analyses in their respective cohorts. A.A., G.W.M., S.Ripatti, M.P., E.D., S.B., T.F., J.v.M., H.P., H.A., B.Pierce., T.L., D.I.B., B.M.P., S.A.G., P.A., L.M., W.H.O., K.D., O.S., A.Battle, M.Scholz, G.G., T.E., W.A., F.B., J.D., M.E., B.P.F., M.G., B.T.H., M.K., Y.K., J.C.K., P.K., K.K., M.L., U.M.M., H.M., Y.M., M.M.-N., M.Nauck, M.G.N., B.W.J.H.P., O.T.R., O.Rotzschke, E.P.S., C.D.A.S., M.Stumvoll, P.S., P.A.C.’t.H., J.T., A.Tönjes, J.v.D., M.v.I., J.H.V., U.Völker and C.W. provided data used in the study. B.Z., H.K., Z.K., J.Kronberg, S.Rüeger, E.P., S.L., J.Y., F.Z., P.M.V., J.P., T.Q., R.W., H.K., M.Scholz and G.G. participated in downstream analyses. S.Y., H.B., R.O., D.H.d.V. and M.G.P.v.d.W. ran replication analyses in scRNA-seq cohorts. A.W.H., J.A.H. and J.P. generated scRNA-seq replication data. H.K., A.Teumer., M.G., M.G.N., J.P., Z.K., J.Y., P.M.V., M.Scholz, G.G., J.P., S.A.G. and P.A.C.’t.H. contributed to writing and revising the manuscript. J.K.P. provided Supplementary Equations for interpretation of results. H.B. and M.Swertz created the website to host results. H-J.W., M.J.B. and P.D. contributed equally to this work. The BIOS Consortium contributed the subset of whole-blood data used in discovery analyses. The i2QTL Consortium contributed trans-eQTL and eQTS replication analyses of iPSCs.
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B.M.P. serves on the Steering Committee for the Yale Open Data Access Project funded by Johnson & Johnson. This activity is unrelated to this work. The rest of the authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Cis-eQTL replication in GTEx v7 tissues.
Cis-eQTL replication in GTEx v7 tissues. For this analysis, the most significant cis-eQTL SNP for each gene was tested in the available post-mortem tissues in GTEx v7. Since GTEx was part of our discovery meta-analysis, the cis-eQTL discovery analysis was repeated while excluding GTEx whole blood, identifying 16,963 lead cis-eQTL effects that were subsequently replicated in each GTEx tissue. Left: while the majority of the 16,963 cis-eQTL were tested in the GTEx replication study, a relatively small fraction had an FDR < 0.05. Middle: of those cis-eQTL showing a replication FDR < 0.05, allelic directions were highly consistent with the discovery meta-analysis. Right: sample sizes of GTEx tissues. Limited replication rates at FDR < 0.05 were probably due to the relatively small sample size per GTEx tissue.
Extended Data Fig. 2 Dot-plot showing the locations of the trans-eQTL effects identified in discovery meta-analysis and their association P-values (-log10 scale).
Dot-plot showing the locations of the trans-eQTL effects identified in discovery meta-analysis (weighted Z-score meta-analysis on Spearman correlation) and their respective two-sided association P-values in -log10 scale. SNP positions are shown on the x-axis and gene locations on the y-axis, each dot shows one significant trans-eQTL effect (FDR < 0.05). Vertical bands appear where a single genomic locus affects many genes in trans, while horizontal bands illustrate genes affected by many SNPs.
Extended Data Fig. 3 Overview of GWAS trait classes in eQTS analysis.
Overview of tested and significant (FDR < 0.05) GWAS trait classes in eQTS analysis.
Supplementary information
Supplementary Information
Supplementary Figs. 1–20, Note and Equations.
Supplementary Tables
Supplementary Tables 1–33.
Supplementary Data 1
Cis-eQTL lead SNP replication in the GTEx project.
Supplementary Data 2
Significant trans-eQTL effects, replication results in purified cell types and cell lines.
Supplementary Data 3
Trans-eQTL replication results in GTEx tissues.
Supplementary Data 4
Putative mechanisms of trans-eQTL.
Supplementary Data 5
Results of eQTS analysis, replications in cell lines.
Supplementary Data 6
eQTS replication analyses in the GTEx European subset of samples.
Supplementary Data 7
eQTS replication analyses in all GTEx samples.
Supplementary Data 8
Effect of cell type composition on trans-eQTL.
Supplementary Data 9
Results of cell type interaction analyses for trans-eQTL.
Source data
Source Data Fig. 2
Statistical source data for Fig. 2a–c.
Source Data Fig. 3
Statistical source data for Fig. 3a, right.
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Võsa, U., Claringbould, A., Westra, HJ. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 53, 1300–1310 (2021). https://doi.org/10.1038/s41588-021-00913-z
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DOI: https://doi.org/10.1038/s41588-021-00913-z
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Integrated multi-omics analyses revealed the association between rheumatoid arthritis and colorectal cancer: MYO9A as a shared gene signature and an immune-related therapeutic target
BMC Cancer (2024)
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Causal roles and clinical utility of cardiovascular proteins in colorectal cancer risk: a multi-modal study integrating mendelian randomization, expression profiling, and survival analysis
BMC Medical Genomics (2024)
