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Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.
Open accessPreprint — published as 10.1038/s41588-020-0682-6 · Paper page
Abstract
The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here, we estimated the effects of 1002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naive phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programmes showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of our approach in identifying and prioritising potential therapeutic targets.
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“Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1–4.”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [2]
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“Zheng et al.16 used MR and colocalisation analysis to examine associations of 1002 plasma proteins with 153 diseases and 72 disease-related risk factors, and identified 413 protein–trait associations supported by MR, 130 (31.5%) of which were not supported by the colocalisation analysis.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Zheng et al. [16]
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“We performed a PWAS using FUSION45. After FDR correction and pleiotropy filtering50,51, 125 pQTL for 108 proteins were significantly associated with AD.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [51]
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“With increasing availability of large-scale phenotyping (and genotyping), MR is being widely applied to quantitative traits (e.g. markers or adiposity, such as body mass index 13-16 ; blood lipids such as HDL-cholesterol 17-19 ; inflammation-related proteins such as interleukin-6 receptor 20,21 12 ) with the aim of discovering modifiable risk factors for disease that are causal and amenable to the”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [15]
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“The latter estimate represents the direct (that is, unmediated) effect of the exposure on the outcome.”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [84]
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Cites
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
Self-citationThe Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959
Self-citationSzklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw937 · Cited by 6,980
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Tests for comparing elements of a correlation matrix
Steiger, J. H. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.87.2.245 · Cited by 4,621
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G. (2008) · Statistics in Medicine · doi:10.1002/sim.3034 · Cited by 4,618
Self-citationGiambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261
Research works printed but not linked: 1
- 10.1037//0033-2909.87.2.245 · DOI · Paper page
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Zhou, W., Kanai, M., Wu, K.-H., Rasheed, H., Tsuo, K., Hirbo, J. B., Wang, Y., Bhattacharya, A., Zhao, H., Namba, S., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100192 · Cited by 426
Self-citationZhang, J., Dutta, D., Köttgen, A., Tin, A., Schlosser, P., Grams, M. E., Harvey, B., CKDGen Consortium, Yu, B., Boerwinkle, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01051-w · Cited by 373
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Self-citationRosa, M., Chignon, A., Li, Z., Boulanger, M., Arsenault, B. J., Bossé, Y., Thériault, S., & Mathieu, P. (2019) · npj Genomic Medicine · doi:10.1038/s41525-019-0097-4 · Cited by 169
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Self-citationYang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A. (2019) · doi:10.1101/19009605 · Cited by 85
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Kibinge, N., Relton, C. L., Gaunt, T. R., & Richardson, T. G. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.04.007 · Cited by 69
Self-citationBourgault, J., Abner, E., Manikpurage, H. D., Pujol‐Gualdo, N., Laisk, T., Gobeil, É., Gagnon, É., Girard, A. S., Mitchell, P. L., Thériault, S., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.01.028 · Cited by 75
Goudswaard, L. J., Bell, J. A., Hughes, D. A., Corbin, L. J., Walter, K., Smith, G. D., Soranzo, N., Danesh, J., Angelantonio, E. D., Ouwehand, W. H., et al. (2021) · International Journal of Obesity · doi:10.1038/s41366-021-00896-1 · Cited by 73
Self-citationYazdanpanah, N., Yazdanpanah, M., Wang, Y., Forgetta, V., Pollak, M., Polychronakos, C., Richards, J. B., & Manousaki, D. (2021) · Diabetes Care · doi:10.2337/dc21-1049 · Cited by 67
Guo, Y. X., Chen, S.-D., You, J., Huang, S.-Y., Chen, Y., Zhang, Y., Wang, L., He, X., Deng, Y., Zhang, Y.-R., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01924-6 · Cited by 71
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Niu, L., Stinson, S. E., Holm, L. A., Lund, M. A. V., Fonvig, C. E., Cobuccio, L., Meisner, J., Juel, H. B., Fadista, J., Thiele, M. S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02089-2 · Cited by 74
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Sanchez‐Roige, S., & Palmer, A. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0609-7 · Cited by 74
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Daghlas, I., & Gill, D. (2023) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2023.5 · Cited by 65
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Namba, S., Konuma, T., Wu, K.-H., Zhou, W., Initiative, G. B. M.-A., & Okada, Y. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100190 · Cited by 66
Clinlabomics: leveraging clinical laboratory data by data mining strategies
Wen, X., Leng, P., Wang, J., Yang, G., Zu, R., Jia, X., Zhang, K., Mengesha, B. A., Huang, J. H., Wang, D., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04926-1 · Cited by 63
Rottner, A. K., Ye, Y., Navarro‐Guerrero, E., Rajesh, V., Pollner, A., Bevacqua, R. J., Yang, J., Spigelman, A. F., Baronio, R., Bautista, A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01261-2 · Cited by 63
Ahn, K., Penn, R. B., Rattan, S. C., Panettieri, R. A., Voight, B. F., & An, S. S. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202205-0951oc · Cited by 64
Luo, S., Wong, I. C. K., Chui, C. S. L., Zheng, J., Huang, Y., Schooling, C. M., & Yeung, S. L. A. (2023) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(23)00085-5 · Cited by 63
Self-citationEpiGraphDB: a database and data mining platform for health data science
Liu, Y., Elsworth, B., Erola, P., Haberland, V., Hemani, G., Lyon, M., Zheng, J., Lloyd, O. C., Vabistsevits, M., & Gaunt, T. R. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa961 · Cited by 60
Self-citationWatts, E. L., Perez‐Cornago, A., Fensom, G. K., Smith‐Byrne, K., Noor, U., Andrews, C. D., Gunter, M. J., Holmes, M. V., Martin, R. M., Tsilidis, K. K., et al. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac124 · Cited by 59
Self-citationPrapiadou, S., Živković, L., Thorand, B., George, M. J., van der Laan, S. W., Malik, R., Herder, C., Köenig, W., Ueland, T., Kleveland, O., et al. (2023) · Circulation · doi:10.1161/circulationaha.123.064974 · Cited by 63
Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci
Neumeyer, S., Hemani, G., & Zeggini, E. (2019) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2019.10.004 · Cited by 55
Self-citationTimmers, P. R. H. J., Tiys, E., Sakaue, S., Akiyama, M., Kiiskinen, T., Zhou, W., Hwang, S., Yao, C., Biobank Japan Project, Kamatani, Y., et al. (2022) · Nature Aging · doi:10.1038/s43587-021-00159-8 · Cited by 59
Yang, C., Fagan, A. M., Perrin, R. J., Rhinn, H., Harari, O., & Cruchaga, C. (2022) · Genome Medicine · doi:10.1186/s13073-022-01140-9 · Cited by 53
Fang, S., Yarmolinsky, J., Gill, D., Bull, C. J., Perks, C. M., the PRACTICAL Consortium, Davey Smith, G., Gaunt, T. R., & Richardson, T. G. (2023) · PLOS Medicine · doi:10.1371/journal.pmed.1003988 · Cited by 55
Self-citationGe, Y., Ou, Y., Deng, Y., Wu, B., Liu, Y., Zhang, Y.-R., Chen, S.-D., Huang, Y., Dong, Q., Tan, L., et al. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.11.002 · Cited by 56
Self-citationCoding and regulatory variants are associated with serum protein levels and disease
Emilsson, V., Guðmundsdóttir, V., Guðjónsson, A., Jónmundsson, Þ., Jonsson, B. G., Karim, M. A., Ilkov, M., Staley, J. R., Gudmundsson, E. F., Launer, L. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28081-6 · Cited by 61
Self-citationEvaluating the causal effect of circulating proteome on the risk of osteoarthritis-related traits
Zhang, Y., Xie, J., Wen, S., Cao, P., Xiao, W., Zhu, J., Li, S., Wang, Z., Cen, H., Zhu, Z., et al. (2023) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2023-224459 · Cited by 52
Circulating Proteins Influencing Psychiatric Disease: A Mendelian Randomization Study
Lu, T., Forgetta, V., Greenwood, C. M. T., Zhou, S., & Richards, J. B. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.08.015 · Cited by 54
Proteomic cardiovascular risk assessment in chronic kidney disease
Deo, R., Dubin, R. F., Ren, Y., Murthy, A. C., Wang, J., Zheng, H., Zheng, Z., Feldman, H. I., Shou, H., Coresh, J., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad115 · Cited by 57
Harbaum, L., Rhodes, C. J., Wharton, J., Lawrie, A., Karnes, J. H., Desai, A. A., Nichols, W. C., Humbert, M. J. C., Montani, D., Girerd, B., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202109-2106oc · Cited by 50
Wu, Y., Qi, T., Wray, N. R., Visscher, P. M., Zeng, J., & Yang, J. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100344 · Cited by 53
Genetics of chronic respiratory disease
Sayers, I., John, C., Chen, J., & Hall, I. P. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00695-0 · Cited by 54
Cui, H., Zhang, W., Zhang, L., Qu, Y., Xu, Z., Tan, Z., Yan, P., Tang, M., Yang, C., Wang, Y., et al. (2024) · PLoS Medicine · doi:10.1371/journal.pmed.1004362 · Cited by 57
Self-citationWang, C., Yang, C., Western, D., Ali, M., Wang, Y., Phuah, C., Budde, J., Wang, L., Gorijala, P., Timsina, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01973-7 · Cited by 53
Cipriani, V., Tierney, A. L., Griffiths, J. R., Zuber, V., Sergouniotis, P. I., Yates, J. R. W., Moore, A. T., Bishop, P. N., Clark, S. J., & Unwin, R. D. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.05.015 · Cited by 53
Walker, V. M., Zheng, J., Gaunt, T. R., & Smith, G. D. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-024910 · Cited by 44
Self-citationPlasma protein and venous thromboembolism: prospective cohort and mendelian randomisation analyses
Yuan, S., Titova, O. E., Zhang, K., Gou, W., Schillemans, T., Natarajan, P., Chen, J., Li, X., Åkesson, A., Bruzelius, M., et al. (2023) · British Journal of Haematology · doi:10.1111/bjh.18679 · Cited by 44
Application and challenges of nitrogen heterocycles in PROTAC linker
Li, Y., Qu, J., Jiang, L., Peng, X., Wu, K., Chen, M., Peng, Y., & Cao, X. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116520 · Cited by 45
Metadata
- Authors
- Zheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., Robinson, J., Richardson, T. G., Staley, J. R., Elsworth, B., Burgess, S., Sun, B. B., Danesh, J., Runz, H., Maranville, J. C., Martin, H. M., Yarmolinsky, J., Laurin, C., Holmes, M. V., Liu, J., Estrada, K., Santos, R., McCarthy, L., Waterworth, D., Nelson, M. R., Hemani, G., Smith, G. D., Butterworth, A. S., Scott, R. A., & Gaunt, T. R.
- Affiliations
- University of Bristol (GB)
- GlaxoSmithKline (United States) (US)
- GlaxoSmithKline (United Kingdom) (GB)
- University of Cambridge (GB)
- Biogen (United States) (US)
- University of Edinburgh (GB)
- National Institute for Health and Care Research (GB)
- Corresponding author
- Zheng, Jie
- Scott, Robert A.
- Gaunt, Tom R.
- ORCID
- Zheng, Jie
- Haberland, Valeriia
- Baird, Denis
- Walker, Venexia
- Haycock, Philip
- Gutteridge, Alex
- Erola, Pau
- Liu, Yi
- Luo, Shan
- Robinson, Jamie
- Richardson, Tom G.
- Staley, James R.
- Elsworth, Benjamin
- Burgess, Stephen
- Sun, Benjamin B.
- Danesh, John
- Runz, Heiko
- Maranville, Joseph C.
- Martin, Hannah M.
- Yarmolinsky, James
- Laurin, Charles
- Holmes, Michael V.
- Liu, Jimmy
- Estrada, Karol
- Santos, Rita
- Waterworth, Dawn
- Nelson, Matthew R.
- Hemani, Gibran
- Smith, George Davey
- Butterworth, Adam S.
- Scott, Robert A.
- Gaunt, Tom R.
- Venue
- Nature Genetics
- Publisher
- openRxiv
- Published
- 2019-05-05
- Volume
- 52
- Issue
- 10
- Pages
- 1122-1131
- ISSN
- 1061-4036 (ISSN-L)
- PMC
- PMC7610464
- OpenAlex
- W3083496521
- Cited by
- 812
- References
- 91
- Citations by year
- 2026: 128 · 2025: 198 · 2024: 212 · 2023: 114 · 2022: 76 · 2021: 56 · 2020: 21 · 2019: 7
- FWCI
- 39.51
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (acceptedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Pfizer
- AstraZeneca
- GlaxoSmithKline
- Sanofi
- Biogen
- NHS Blood and Transplant (NIHR BTRU-2014-10024)
- Wellcome Trust (204623/Z/16/Z, 102215/2/13/2), 208806, Z/17/Z, 217065/Z/19/Z, 102215, 208806/Z/17/Z, BTRU-2014-10024, 217065)
- Scottish Government
- University Hospitals Bristol NHS Foundation Trust
- Cancer Research UK (C18281/, 29019, C18281 / A19169, 10024, MC_UU_00011/1, 20138, A19169, C52724/A20138, 19169, MC_UU_00011/4)
- National Institute for Health and Care Research (C18281/A19169, NIHR BTRU‐2014‐10024, BTRU-2014–10024, RG/18/13/33946, MC_UU_00011/4, RG/13/13/30194, MC_UU_00011/1)
- British Heart Foundation (MC_UU_00011/1, RG/18/13/33946, 18/23/33512, SP/09/002, RG/13/13/30194, FS/18/23/33512)
- Department of Health and Social Care
- Royal Society (208806/Z/17/Z, Z/17/Z)
- University of Cambridge
- University of Oxford
- University of Bristol (MC_UU_00011/4, C18281/A19169, MC_UU_00011/1, 102215/2/13/2)
- Public Health Agency
- Cambridge University Hospitals
- University of Hong Kong
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- NIHR BioResource
- Elizabeth Blackwell Institute for Health Research, University of Bristol
- Medical Research Council (102215, RG/13/13/30194, MC_UU_00011/4, SP/09/002, ref: 102215/2/13/2, MR/L003120/1, RG/18/13/33946, 102215/2/13/2, MC_PC_15018, MC_UU_00011/1, MR/L003120, MC_PC_19009, MC_UU_00011/1 and MC_UU_00011/4, MC_UU_00011)
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council (102215/2/13/2)
- Health and Social Care Research and Development Division
- NIHR Cambridge Biomedical Research Centre (BTRU-2014-10024, MR/L003120/1)
- Health Data Research UK
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- plasma proteome, plasma protein levels, phenome-wide association study, Mendelian randomization, colocalization, genetic confounding, linkage disequilibrium, complex diseases, drug target prioritization, drug development
- MeSH terms
- Blood Proteins, Humans, Phenotype, Genetic Predisposition to Disease, Proteome, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Mendelian Randomization Analysis
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