The MR-Base platform supports systematic causal inference across the human phenome.
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Authors
Hemani, Gibran
Zheng, Jie
Elsworth, Benjamin
Wade, Kaitlin H
Haberland, Valeriia
Baird, Denis
Laurin, Charles
Bowden, Jack
Langdon, Ryan
Tan, Vanessa Y
Yarmolinsky, James
Shihab, Hashem A
Timpson, Nicholas J
Evans, David M
Relton, Caroline
Martin, Richard M
Davey Smith, George
Gaunt, Tom R
Haycock, Philip C
Publication Date
2018-05-30Journal Title
eLife
ISSN
2050-084X
Publisher
eLife Sciences Publications
Volume
7
Number
e34408
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., et al. (2018). The MR-Base platform supports systematic causal inference across the human phenome.. eLife, 7 (e34408) https://doi.org/10.7554/eLife.34408
Abstract
Results 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 (<ext-link ext-link-type="uri" xlink:href="http://www.mrbase.org">http://www.mrbase.org</ext-link>): 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.
Keywords
GWAS, Mendelian randomization, causal inference, computational biology, human, human biology, medicine, systems biology
Sponsorship
Supported 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).
Funder references
Medical Research Council (MR/L003120/1)
British Heart Foundation (None)
Medical Research Council (MC_UU_00002/7)
Wellcome Trust (204623/Z/16/Z)
Identifiers
External DOI: https://doi.org/10.7554/eLife.34408
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280231
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