The MR-Base platform supports systematic causal inference across the human phenome.

Authors
Hemani, Gibran 
Zheng, Jie 
Elsworth, Benjamin 
Wade, Kaitlin H 
Haberland, Valeriia 

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Type
Article
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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.

Publication Date
2018-05-30
Online Publication Date
2018-03-30
Acceptance Date
2018-03-28
Keywords
GWAS, Mendelian randomization, causal inference, computational biology, human, human biology, medicine, systems biology
Journal Title
eLife
Journal ISSN
2050-084X
2050-084X
Volume Title
7
Publisher
eLife Sciences Publications
Sponsorship
Medical Research Council (MR/L003120/1)
British Heart Foundation (None)
Medical Research Council (MC_UU_00002/7)
Wellcome Trust (204623/Z/16/Z)
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).