Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.
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The EPIC-Norfolk data can be requested by bona fide researchers for specified scientific purposes via the study website (https://www.mrc-epid.cam.ac.uk/research/studies/epic-norfolk/). Data will either be shared through an institutional data sharing agreement or arrangements will be made for analyses to be conducted remotely without the need for data transfer.
Fine-mapped summary statistics for protein-coding regions can be found at https://doi.org/10.5281/zenodo.7576293. The genome-wide summary statistics resulting from the meta-analysis between discovery and replication samples (n = 2,887) can be downloaded for all protein targets included in this study from https://omicscience.org/.
GWAS for anthropometric phenotypes have been conducted using the UK Biobank resource (application no. 44448). Access to the UK Biobank genotype and phenotype data is open to all approved health researchers (http://www.ukbiobank.ac.uk/).
GWAS catalog summary statistics (v.1.0.2) were downloaded (March 2022) from https://www.ebi.ac.uk/gwas/api/search/downloads/studies_alternative. GTEx variant-gene cis-eQTL associations from each tissue were downloaded (January 2020) from https://console.cloud.google.com/storage/browser/gtex-resources. Open GWAS summary statistics were accessed via ieugwasr package in R v.3.6.0.
Associated code and scripts for the analysis are available on GitHub (https://github.com/MRC-Epid/pGWAS_Olink_EPIC).
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The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) received funding from the Medical Research Council (MR/N003284/1 MC-UU_12015/1 and MC_UU_00006/1, to N.J.W.) and Cancer Research UK (C864/A14136. to N.J.W.). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048, to N.J.W.). We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge, including the EPIC-Norfolk investigators, the study coordination team, the epidemiology field, data and laboratory teams who have enabled this research. This work was supported in part by MRC Rapid Call (MC_PC_21036, to N.J.W. and C.L.) and HDRUK Multi-Omics (G107794, to C.L.) grants and the UKRI/NIHR Strategic Priorities Award in Multimorbidity Research for the Multimorbidity Mechanism and Therapeutics Research Collaborative (MR/V033867/1, to C.L.). Proteomics measurements were also supported by a collaboration agreement between the University of Cambridge and Olink. We thank P. Pettingill, I. Grundberg and J. Kenyon for their support with QC of the proteomic data. M.K. is supported by Gates Cambridge Trust. J.C.-Z. is supported by a 4-year Wellcome Trust PhD Studentship and the Cambridge Trust. C.L., E.W., M.P., N.D.K. and N.J.W. are funded by the Medical Research Council (MC_UU_00006/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any author accepted manuscript version arising. The authors thank the MVP staff, researchers and volunteers, who contributed to MVP and especially participants who previously served their country in the military and generously agreed to enroll in the study (see https://www.research.va.gov/mvp/ for more details). We thank F. Paul, A. Hingorani and S. Gordon for sharing their expertise on disease-specific examples. We thank Gabi Kastenmüller and Maria Anna Wörheide for their support in making genome-wide summary statistics from this study available on omicscience.org.
E.W. is now an employee of AstraZeneca. All other authors have no competing interests.
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Supplementary Notes 1 and 2, Supplementary Figs. 1 and 2 and descriptions of Supplementary Tables 1–9 and Supplementary Data.
Supplementary Tables 1–9
Supplementary Table 1. Demographics of the EPIC-Norfolk study. Supplementary Table 2. List of diseases for discovery (n = 425) and replication (n = 706) cases. Supplementary Table 3. Independent credible sets for protein targets at protein-encoding loci (±500 kb) identified by fine-mapping. Supplementary Table 4. Variance explained by all cis-pQTLs for each protein with at least one cis-pQTL identified in this study. Supplementary Table 5. Results from colocalization analysis with gene eQTLs from GTEx v.8 release at the protein-encoding loci (±500 kb). Supplementary Table 6. cis-pQTLs with sex-differential effects at a Bonferroni-corrected significance threshold (P < 0.05 / 1,553). Supplementary Table 7. Protein-target–phenotype pairs with strong evidence of colocalization at the protein-encoding locus (±500 kb). Supplementary Table 8. pQTL mapping of candidate causal genes at previously reported GWAS loci from GWAS catalog. Supplementary Table 9. Phenotypic convergence between pQTL colocalization and rare LoF gene-burden associations.
Protein–disease network as a Cytoscape session. Results from phenome-wide colocalization at protein-coding loci (±500 kb) are shown. For simplicity, only proteins with at least one binary outcome (mainly diseases) association are included. Proteins are presented by a square, binary outcomes are presented as large circles and continuous outcomes are presented as small circles. The color for the circles indicates the trait category. Edges between proteins and phenotypes represent strong evidence for a shared genetic signal (PP > 80% and LD between regional sentinel variants >0.8). Effect directions are indicated by the line type (solid, higher protein abundance, increased risk; dashed, higher protein abundance, reduced risk) and are derived based on the lead cis-pQTL at the corresponding locus. The full list of colocalization results is in Supplementary Table 7.
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Koprulu, M., Carrasco-Zanini, J., Wheeler, E. et al. Proteogenomic links to human metabolic diseases. Nat Metab 5, 516–528 (2023). https://doi.org/10.1038/s42255-023-00753-7