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 targets.
“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]
The Discussion (page unavailable) says that combining genetic instruments for plasma proteins with disease-phenotype data can reveal potentially causal associations and help prioritize drug targets. That supports the disease-mechanism and drug-target parts of the claim. The paper does not report identifying clinical biomarkers; the only retrieved use of “biomarkers” is in a reference title. (LLM, conf=0.87)
Experimental — accuracy not yet measuredZheng et al. 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.
“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]
The study performed a PWAS using FUSION. After FDR correction and pleiotropy filtering, 125 pQTL for 108 proteins were significantly associated with AD.
“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]
MR is being widely applied to quantitative traits with the aim of discovering modifiable risk factors for disease that are causal and amenable to therapeutic modification.
“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]
The estimate from multivariable Mendelian randomization including the mediator as an additional exposure represents the direct (unmediated) effect of the exposure on the outcome.
“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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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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