Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31629
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dc.contributor.authorHemani, G-
dc.contributor.authorZheng, J-
dc.contributor.authorElsworth, B-
dc.contributor.authorWade, KH-
dc.contributor.authorHaberland, V-
dc.contributor.authorBaird, D-
dc.contributor.authorLaurin, C-
dc.contributor.authorBurgess, S-
dc.contributor.authorBowden, J-
dc.contributor.authorLangdon, R-
dc.contributor.authorTan, VY-
dc.contributor.authorYarmolinsky, J-
dc.contributor.authorShihab, HA-
dc.contributor.authorTimpson, NJ-
dc.contributor.authorEvans, DM-
dc.contributor.authorRelton, C-
dc.contributor.authorMartin, RM-
dc.contributor.authorDavey Smith, G-
dc.contributor.authorGaunt, TR-
dc.contributor.authorHaycock, PC-
dc.date.accessioned2025-07-28T09:31:58Z-
dc.date.available2025-07-28T09:31:58Z-
dc.date.issued2018-05-30-
dc.identifierORCiD: Gibran Hemani https://orcid.org/0000-0003-0920-1055-
dc.identifierORCiD: Jie Zheng https://orcid.org/0000-0002-6623-6839-
dc.identifierORCiD: Kaitlin H. Wade https://orcid.org/0000-0003-3362-6280-
dc.identifierORCiD: Valeriia Haberland https://orcid.org/0000-0003-4600-6013-
dc.identifierORCiD: Stephen Burgess https://orcid.org/0000-0001-5365-8760-
dc.identifierORCiD: Vanessa Y. Tan https://orcid.org/0000-0001-7938-127X-
dc.identifierORCiD: Caroline Relton https://orcid.org/0000-0003-2052-4840-
dc.identifierORCiD: Richard M. Martin https://orcid.org/0000-0002-7992-7719-
dc.identifierORCiD: George Davey Smith https://orcid.org/0000-0002-1407-8314-
dc.identifierORCiD: Tom R. Gaunt https://orcid.org/0000-0003-0924-3247-
dc.identifierORCiD: Philip C. Haycock https://orcid.org/0000-0001-5001-3350-
dc.identifierArticle number: e34408-
dc.identifier.citationHemani, G. et al. (2018) 'The MR-base platform supports systematic causal inference across the human phenome', eLife, 7, e34408, pp. 1 - 29. doi: 10.7554/eLife.34408.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31629-
dc.descriptionFigures and data are available online at: https://elifesciences.org/articles/34408/figures#content .en_US
dc.description.abstractResults from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.en_US
dc.description.sponsorshipSupported by Cancer Research UK grant C18281/A19169 (the Integrative Cancer Epidemiology Programme) and the Roy Castle Lung Cancer Foundation (2013/18/Relton). The Medical Research Council Integrative Epidemiology Unit is supported by grants MC_UU_12013/1, MC_UU_12013/2 and MC_UU_12013/8. PCH is supported by a Cancer Research UK Population Research Postdoctoral Fellowship (C52724/A20138). Jack Bowden is supported by a MRC Methodology Research Fellowship (grant MR/N501906/1). DME supported by the NHMRC APP1125200, APP1137714. GH is supported by Wellcome (208806/Z/17/Z).en_US
dc.format.extent1 - 29-
dc.format.mediumElectronic-
dc.language.isoen_USen_US
dc.publishereLife Sciences Publicationsen_US
dc.rightsCreative Commons Attribution 4.0 International-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.titleThe MR-base platform supports systematic causal inference across the human phenomeen_US
dc.typeArticleen_US
dc.date.dateAccepted2018-03-28-
dc.identifier.doihttps://doi.org/10.7554/eLife.34408-
dc.relation.isPartOfeLife-
pubs.publication-statusPublished-
pubs.volume7-
dc.identifier.eissn2050-084X-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dcterms.dateAccepted2018-03-28-
dc.rights.holderHemani et al.-
Appears in Collections:Dept of Computer Science Research Papers

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