Abstract

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

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Acknowledgements

A. Day-Williams, J. McElwee, D. Diogo, W. Astle, E. Di Angelantonio, E. Birney, A. Richard, J. Mason and M. Inouye commented on the manuscript, and M. Sharp helped with mapping drug indications to GWAS traits. We thank INTERVAL study participants; staff at recruiting NHSBT blood donation centres; and the INTERVAL Study Co-ordination team, Operations Team (led by R. Houghton and C. Moore) and Data Management Team (led by M. Walker). Funding sources are listed in the Supplementary Information.

Reviewer information

Nature thanks T. Lappalainen, M. McCarthy and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Joseph C. Maranville
    •  & Robert M. Plenge

    Present address: Celgene Inc., Cambridge, MA, USA

    • Heiko Runz

    Present address: Biogen Inc., Cambridge, MA, USA

  1. These authors contributed equally: Benjamin B. Sun, Joseph C. Maranville, James E. Peters.

  2. These authors jointly supervised this work: Heiko Runz, Adam S. Butterworth.

Affiliations

  1. MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    • Benjamin B. Sun
    • , James E. Peters
    • , David Stacey
    • , James R. Staley
    • , James Blackshaw
    • , Stephen Burgess
    • , Tao Jiang
    • , Ellie Paige
    • , Praveen Surendran
    • , Clare Oliver-Williams
    • , Mihir A. Kamat
    • , Bram P. Prins
    • , Narinder Bansal
    • , Angela M. Wood
    • , Dirk S. Paul
    • , John Danesh
    •  & Adam S. Butterworth
  2. MRL, Merck & Co., Inc., Kenilworth, NJ, USA

    • Joseph C. Maranville
    • , An Chi
    • , Caroline S. Fox
    • , Robert M. Plenge
    •  & Heiko Runz
  3. British Heart Foundation Cambridge Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, Cambridge, UK

    • James E. Peters
    • , Nicholas W. Morrell
    • , Willem H. Ouwehand
    • , Nicole Soranzo
    •  & John Danesh
  4. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

    • Stephen Burgess
  5. National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia

    • Ellie Paige
  6. Homerton College, Cambridge, UK

    • Clare Oliver-Williams
  7. SomaLogic Inc, Boulder, CO, USA

    • Sheri K. Wilcox
    • , Erik S. Zimmerman
    •  & Nebojsa Janjic
  8. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

    • Narinder Bansal
  9. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    • Sarah L. Spain
  10. Division of Respiratory Medicine, Department of Medicine, University of Cambridge, Cambridge, UK

    • Nicholas W. Morrell
  11. NIHR Cambridge Biomedical Research Centre/BioResource, Cambridge University Hospitals, Cambridge, UK

    • John R. Bradley
  12. National Health Service (NHS) Blood and Transplant and Radcliffe Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK

    • David J. Roberts
  13. BRC Haematology Theme and Department of Haematology, Churchill Hospital, Oxford, UK

    • David J. Roberts
  14. Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    • Willem H. Ouwehand
    •  & Nicole Soranzo
  15. National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK

    • Willem H. Ouwehand
  16. Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    • Willem H. Ouwehand
    • , Nicole Soranzo
    •  & John Danesh
  17. NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    • Willem H. Ouwehand
    • , Nicole Soranzo
    • , John Danesh
    •  & Adam S. Butterworth
  18. JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK

    • John A. Todd
  19. Department of Physiology and Biophysics, Weill Cornell Medicine–Qatar, Doha, Qatar

    • Karsten Suhre

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Contributions

Conceptualization and experimental design: J.D., A.S.B., B.B.S., H.R., R.M.P.; methodology: B.B.S., A.S.B., J.C.M., J.E.P., H.R., S.B.; conducted experimental work: N.J., S.K.W., E.S.Z.; analysis: B.B.S., J.C.M., J.E.P., D.S., J.B., J.R.S., T.J., E.P., P.S., C.O.-W., M.A.K., S.K.W., A.C., N.B., S.L.S.; contributed reagents, materials, protocols or analysis tools: N.J., S.K.W., E.S.Z., J.B., M.A.K., J.R.S., B.P.P.; supervision: A.S.B., H.R., J.D., R.M.P., C.S.F., D.S.P., A.M.W.; writing: A.S.B., J.E.P., B.B.S., J.C.M., H.R., J.D., J.A.T., N.S., K.S.; creation of the INTERVAL BioResource: J.R.B., D.J.R.,W.H.O., N.W.M., J.D.; funding: N.W.M., J.R.B., D.J.R., W.H.O., H.R., R.M.P., J.D.; all authors critically reviewed the manuscript.

Competing interests

The authors declare the following competing interests: A.C., CSF-Merck employee; N.J., S.K.W., SomaLogic Inc employees and stakeholders; E.S.Z., SomaLogic Inc employee; J.C.M., R.M.P., Merck employees during this study, now Celgene employees; H.R., Merck employee during this study; J.E.P., travel and accommodation expenses and hospitality from Olink to speak at Olink-sponsored academic meetings; A.S.B., grants from Merck, Pfizer, Novartis, Biogen and Bioverativ and personal fees from Novartis; J.D., sits on the Novartis Cardiovascular and Metabolic Advisory Board, had grant support from Novartis.

Corresponding authors

Correspondence to John Danesh or Adam S. Butterworth.

Extended data figures and tables

  1. Extended Data Fig. 1

    Flowchart of sample processing and quality control stages for proteomic and genetic measurements before genetic analyses.

  2. Extended Data Fig. 2 Examples of protein targets for which the SOMAmer is highly specific.

    SDS–PAGE with Alexa-647-labelled proteins captured by the IL1RL2 SOMAmer (a) or GP1BA SOMAmer (b). For each protein target, the protein captured by the SOMAmer is compared to the standard. The cognate targets are the only ones with protein visible in the capture lanes, whereas the proteins homologous to the target proteins show no evidence of binding. These experiments were performed once. MW markers, molecular weight markers.

  3. Extended Data Fig. 3 Evidence for the reliability of protein measurements made using the SOMAscan assay.

    a, Distribution of coefficients of variation of all proteins on the SOMAscan assay in each subcohort. b, Spearman’s correlations for all proteins passing QC derived from contemporaneous assay of baseline and two-year samples from 60 participants. c, Scatterplot of pQTL effect size estimates from SOMAscan versus Olink showing all 163 pQTLs tested (top) and the 106 that replicated (bottom). r is Pearson’s correlation coefficient. d, Distribution of inflation factors across proteins that underwent genome-wide association testing, stratified by subcohort and allele frequency (MAF ≥ 5%, MAF < 5%).

  4. Extended Data Fig. 4 The WFIKKN2 region is a trans pQTL for GDF11/8 plasma levels.

    a, Regional association plots of the trans pQTL (sentinel variant rs11079936) for GDF11/8 before and after adjusting for levels of WFIKKN2 (upper panels), and the WFIKKN2 cis pQTL after adjusting for GDF11/8 levels (bottom panel). A similar pattern of association for WFIKKN2 was seen before GDF11/8 adjustment (not shown). b, Attenuation of the GDF11/8 trans pQTL upon adjustment for plasma levels of the cis protein WFIKKN2.

  5. Extended Data Fig. 5 Genetic architecture of the pQTLs.

    pQTL mapping in n = 3,301 individuals. a, Distribution of the predicted consequences of the sentinel pQTL variants compared to matched permuted null sets of variants, stratified by cis and trans. Asterisks indicate empirical enrichment using a permutation test (10,000 permuted sets of non-associated variants) at a Bonferroni-corrected significance value (P < 0.005). Bar height represents the mean proportion of variants within each class and error bars reflect one standard deviation from the mean. b, Number of proteins associated (P < 1.5 × 10−11) with each sentinel variant across the genome.

  6. Extended Data Fig. 6 Enrichment of pQTLs at DNase I hypersensitive sites by tissue or cell type.

    Circle shows enrichment for DNase I hypersensitive sites (‘hotspots’) for each of 55 tissues (183 cell types) available from the ENCODE and Roadmap Epigenomics projects, with tissues or cell types clustered and coloured by anatomical grouping. Some tissues have multiple values due to availability of multiple cell types or multiple tests per cell type. Radial lines show fold-enrichment, while dots around the inside edge of the circle denote statistically significant enrichment at a Bonferroni-corrected significant threshold P < 5 × 10−5. Enrichment testing performed using GARFIELD (which tests enrichment against permuted sets of variants matched for MAF, distance to TSS and LD). pQTL data from n = 3,301 individuals.

  7. Extended Data Fig. 7 Scheme outlining the combined ‘bottom-up’ and ‘top-down’ process used for candidate gene annotation of trans pQTL regions.

    See Methods. GbA, guilt-by-association; KEGG, Kyoto Encyclopedia of Genes and Genomes; OMIM, Online Mendelian Inheritance in Man; STRINGdb, STRING database.

  8. Extended Data Fig. 8 Follow-up of PR3 SOMAmers.

    These experiments were repeated three times independently with similar results. a, SOMAmer pulldowns with purified PR3, A1AT, and PR3–A1AT complex. SOMAmer PRTN3.3514.49.2 enriched the PR3–A1AT complex to a much greater degree than free PR3. Conversely, SOMAmer PRTN3.13720.95.3 enriched free PR3 to a greater degree than the PR3–A1AT complex. b, Solution affinity of PRTN3.3514.49.2 and PRTN3.13720.95.3 for PR3, A1AT, and the PR3–A1AT complex. SOMAmer PRTN3.3514.49.2 has a higher affinity for the PR3–A1AT complex than for free PR3. SOMAmer PRTN3.13720.95.3, on the other hand, has a higher affinity for free PR3 than SOMAmer PRTN3.3514.49.2. c, Competitive binding of SOMAmers PRTN3.13720.95.3 and PRTN3.3514.49.2 to PR3. A limiting amount of radiolabelled PRTN3.13720.95.3 was incubated with 1 nM proteinase-3 and a titration of either cold PRTN3.13720.95.3 or cold PRTN3.3514.49.2.

  9. Extended Data Fig. 9

    Comparison between a randomized controlled trial and Mendelian randomization to assess the causal effect of changes in protein biomarker levels on disease risk.

  10. Extended Data Fig. 10 Characterization of protein targets measured using the SOMAscan assay.

    a, Compartment distribution with annotations of all proteins in the Human Protein Atlas for comparison. b, GO molecular functions.

Supplementary information

  1. Supplementary Information

    This file contains funding details, full Supplementary Table Legends, Supplementary Notes and Supplementary References

  2. Reporting Summary

  3. Supplementary Figure 1

    A three-dimensional interactive plot of sentinel variant-protein associations (red-cis, blue-trans). X-axis (“pQTL position”) represents position of the sentinel variant along chromosomes 1-22. Y-axis (“Protein position”) represents the start position of the gene encoding the protein. Z-axis represents the –log10(p) of the association. Additional details can be viewed when hovering over the points. Clicking on cis/trans in the legend toggles display of points by cis/trans. Additional viewing controls are available at the top right of the window. For clarity, associations with p<10-300 (diamonds) are plotted at -log10(p)=300. The plot is generated using “plotly” R package v4.5.6 (Plotly Technologies Inc., Montréal, Canada)

  4. Supplementary Tables

    Supplementary Tables 1-21 – see Supplementary Information file for full descriptions

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