Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29275
Title: Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases
Authors: Zheng, J
Haberland, V
Baird, D
Walker, V
Haycock, PC
Hurle, MR
Gutteridge, A
Erola, P
Liu, Y
Luo, S
Robinson, J
Richardson, TG
Staley, JR
Elsworth, B
Burgess, S
Sun, BB
Danesh, J
Runz, H
Maranville, JC
Martin, HM
Yarmolinsky, J
Laurin, C
Holmes, MV
Liu, JZ
Estrada, K
Santos, R
McCarthy, L
Waterworth, D
Nelson, MR
Smith, GD
Butterworth, AS
Hemani, G
Scott, RA
Gaunt, TR
Keywords: drug discovery;genetics;high-throughput screening;proteomics
Issue Date: 7-Sep-2020
Publisher: Nature Research
Citation: Zheng, J. et al. (2020) 'Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases', Nature Genetics, 52 (10), pp. 1122 - 1131. doi: 10.1038/s41588-020-0682-6.
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 1,002 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 is widespread in naïve 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 (https://www.epigraphdb.org/pqtl/). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
Description: Data availability: The data (GWAS summary statistics) used in the analyses described here are freely accessible in the MR-Base platform (https://www.mrbase.org/). All our analysis results for 989 proteins against 225 human phenotypes are freely available to browse, query and download in EpiGraphDB (https://www.epigraphdb.org/pqtl/). An application programming interface and R package documented on the website enable users to programmatically access data from the database.
Code availability: The code used in the MR and colocalization analyses described here are freely accessible via our GitHub repository (https://github.com/MRCIEU/epigraphdb-pqtl/). The MR analysis was conducted using TwoSampleMR R package (https://github.com/MRCIEU/TwoSampleMR/). We implemented the colocalization analysis using the coloc R package (created by C. Wallace and colleagues), which can be downloaded at https://cran.r-project.org/web/packages/coloc/index.html/.
The author accepted manuscript was made available on PubMed Central on 7 March 2021 at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610464/ .
URI: https://bura.brunel.ac.uk/handle/2438/29275
DOI: https://doi.org/10.1038/s41588-020-0682-6
ISSN: 1061-4036
Other Identifiers: ORCiD: Jie Zheng https://orcid.org/0000-0002-6623-6839
ORCiD: Valeriia Haberland https://orcid.org/0000-0002-3874-0683
ORCiD: Jamie Robinson https://orcid.org/0000-0001-8721-6514
ORCiD: Tom G. Richardson https://orcid.org/0000-0002-7918-2040
ORCiD: Benjamin Elsworth https://orcid.org/0000-0001-7328-4233
ORCiD: Benjamin B. Sun https://orcid.org/0000-0001-6347-2281
ORCiD: Tom R. Gaunt https://orcid.org/0000-0003-0924-3247
Appears in Collections:Dept of Computer Science Research Papers

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