Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31629
Title: The MR-base platform supports systematic causal inference across the human phenome
Authors: Hemani, G
Zheng, J
Elsworth, B
Wade, KH
Haberland, V
Baird, D
Laurin, C
Burgess, S
Bowden, J
Langdon, R
Tan, VY
Yarmolinsky, J
Shihab, HA
Timpson, NJ
Evans, DM
Relton, C
Martin, RM
Davey Smith, G
Gaunt, TR
Haycock, PC
Issue Date: 30-May-2018
Publisher: eLife Sciences Publications
Citation: Hemani, 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.
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 (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.
Description: Figures and data are available online at: https://elifesciences.org/articles/34408/figures#content .
URI: https://bura.brunel.ac.uk/handle/2438/31629
DOI: https://doi.org/10.7554/eLife.34408
Other Identifiers: ORCiD: Gibran Hemani https://orcid.org/0000-0003-0920-1055
ORCiD: Jie Zheng https://orcid.org/0000-0002-6623-6839
ORCiD: Kaitlin H. Wade https://orcid.org/0000-0003-3362-6280
ORCiD: Valeriia Haberland https://orcid.org/0000-0003-4600-6013
ORCiD: Stephen Burgess https://orcid.org/0000-0001-5365-8760
ORCiD: Vanessa Y. Tan https://orcid.org/0000-0001-7938-127X
ORCiD: Caroline Relton https://orcid.org/0000-0003-2052-4840
ORCiD: Richard M. Martin https://orcid.org/0000-0002-7992-7719
ORCiD: George Davey Smith https://orcid.org/0000-0002-1407-8314
ORCiD: Tom R. Gaunt https://orcid.org/0000-0003-0924-3247
ORCiD: Philip C. Haycock https://orcid.org/0000-0001-5001-3350
Article number: e34408
Appears in Collections:Dept of Computer Science Research Papers

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