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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29275
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dc.contributor.authorZheng, J-
dc.contributor.authorHaberland, V-
dc.contributor.authorBaird, D-
dc.contributor.authorWalker, V-
dc.contributor.authorHaycock, PC-
dc.contributor.authorHurle, MR-
dc.contributor.authorGutteridge, A-
dc.contributor.authorErola, P-
dc.contributor.authorLiu, Y-
dc.contributor.authorLuo, S-
dc.contributor.authorRobinson, J-
dc.contributor.authorRichardson, TG-
dc.contributor.authorStaley, JR-
dc.contributor.authorElsworth, B-
dc.contributor.authorBurgess, S-
dc.contributor.authorSun, BB-
dc.contributor.authorDanesh, J-
dc.contributor.authorRunz, H-
dc.contributor.authorMaranville, JC-
dc.contributor.authorMartin, HM-
dc.contributor.authorYarmolinsky, J-
dc.contributor.authorLaurin, C-
dc.contributor.authorHolmes, MV-
dc.contributor.authorLiu, JZ-
dc.contributor.authorEstrada, K-
dc.contributor.authorSantos, R-
dc.contributor.authorMcCarthy, L-
dc.contributor.authorWaterworth, D-
dc.contributor.authorNelson, MR-
dc.contributor.authorSmith, GD-
dc.contributor.authorButterworth, AS-
dc.contributor.authorHemani, G-
dc.contributor.authorScott, RA-
dc.contributor.authorGaunt, TR-
dc.date.accessioned2024-06-25T08:57:09Z-
dc.date.available2024-06-25T08:57:09Z-
dc.date.issued2020-09-07-
dc.identifierORCiD: Jie Zheng https://orcid.org/0000-0002-6623-6839-
dc.identifierORCiD: Valeriia Haberland https://orcid.org/0000-0002-3874-0683-
dc.identifierORCiD: Jamie Robinson https://orcid.org/0000-0001-8721-6514-
dc.identifierORCiD: Tom G. Richardson https://orcid.org/0000-0002-7918-2040-
dc.identifierORCiD: Benjamin Elsworth https://orcid.org/0000-0001-7328-4233-
dc.identifierORCiD: Benjamin B. Sun https://orcid.org/0000-0001-6347-2281-
dc.identifierORCiD: Tom R. Gaunt https://orcid.org/0000-0003-0924-3247-
dc.identifier.citationZheng, 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.en_US
dc.identifier.issn1061-4036-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/29275-
dc.descriptionData 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.en_US
dc.descriptionCode 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/.-
dc.descriptionThe author accepted manuscript was made available on PubMed Central on 7 March 2021 at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610464/ .-
dc.description.abstractThe 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.en_US
dc.description.sponsorshipJ.Z. is funded by a Vice-Chancellor’s Fellowship from the University of Bristol. This research was also funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/1 and MC_UU_00011/4), GlaxoSmithKline, Biogen and the Cancer Research Integrative Cancer Epidemiology Programme (C18281/A19169). The UK Medical Research Council and Wellcome (grant no. 102215/2/13/2) and the University of Bristol provided core support for ALSPAC. A comprehensive list of grant funding is available on the ALSPAC website (https://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf/). T.R.G. holds a Turing Fellowship with the Alan Turing Institute. G.H. is funded by the Wellcome Trust and the Royal Society (208806/Z/17/Z). M.V.H. is supported by a British Heart Foundation Intermediate Clinical Research Fellowship (FS/18/23/33512) and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. This work has been supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol (G.D.S. and T.R.G.). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by the Elizabeth Blackwell Institute for Health Research University of Bristol and the Medical Research Council Proximity to Discovery award. P.E. is supported by Cancer Research UK (CRUK; C18281/A19169). S.L. is funded by the Bau Tsu Zung Bau Kwan Yeun Hing Research and Clinical Fellowship (200008682.920006.20006.400.01) from the University of Hong Kong. J.D. is funded by a NIHR Senior Investigator award. J.D. sits on the International Cardiovascular and Metabolic advisory board for Novartis (since 2010), the UK Biobank Steering Committee (since 2011), and is a member of the MRC International Advisory Group (ING) London (since 2013), the MRC High Throughput Science ‘Omics Panel’, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the AstraZeneca Genomics advisory board (since 2018). P.C.H. is supported by CRUK Population Research Postdoctoral Fellowship C52724/A20138.en_US
dc.format.extent1122 - 1131-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherNature Researchen_US
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610464/-
dc.rightsCopyright © 2020 Springer Nature. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (see: https://www.springernature.com/gp/open-research/policies/journal-policies), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41588-020-0682-6.-
dc.rights.urihttps://www.springernature.com/gp/open-research/policies/journal-policies-
dc.subjectdrug discoveryen_US
dc.subjectgeneticsen_US
dc.subjecthigh-throughput screeningen_US
dc.subjectproteomicsen_US
dc.titlePhenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseasesen_US
dc.typeArticleen_US
dc.date.dateAccepted2020-07-24-
dc.identifier.doihttps://doi.org/10.1038/s41588-020-0682-6-
dc.relation.isPartOfNature Genetics-
pubs.issue10-
pubs.publication-statusPublished-
pubs.volume52-
dc.identifier.eissn1546-1718-
dc.rights.holderSpringer Nature-
Appears in Collections:Dept of Computer Science Research Papers

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