EpiGraphDB is an analytical platform and database to support data mining in epidemiology. The platform incorporates a graph of causal estimates generated by systematically applying Mendelian randomization to a wide array of phenotypes, and augments this with a wealth of additional data from other bioinformatic sources. EpiGraphDB aims to support appropriate application and interpretation of causal inference in systematic automated analyses of many phenotypes.

epigraphdb is an R package to provide ease of access to EpiGraphDB services. We will refer to epigraphdb as the name of the R package whereas "EpiGraphDB" as the overall platform.


To install the latest development version from github ( devtools is required ):

# install.packages("devtools")

To install a stable version from CRAN:


NOTE: while the package repository is “epigraphdb-r”, the R package name is “epigraphdb”.

Using epigraphdb

epigraphdb provides a simple and intuitive way to query the API, as:

#>   EpiGraphDB v1.0 (API: https://api.epigraphdb.org)
mr(outcome_trait = "Body mass index")
#> # A tibble: 370 x 12
#>    exposure_id exposure_name outcome_id outcome_name estimate      se
#>    <chr>       <chr>         <chr>      <chr>           <dbl>   <dbl>
#>  1 627         Epiandroster… 785        Body mass i…   0.0950 2.28e-3
#>  2 541         X-11787       835        Body mass i…  -0.0578 1.77e-4
#>  3 971         Ulcerative c… 835        Body mass i…  -0.0111 1.76e-4
#>  4 60          Waist circum… 835        Body mass i…   0.861  2.07e-2
#>  5 UKB-a:426   Eye problems… 94         Body mass i…  -1.12   1.90e-2
#>  6 UKB-a:373   Ever depress… 95         Body mass i…  -0.616  4.80e-4
#>  7 29          Birth length  95         Body mass i…  -0.141  5.67e-4
#>  8 350         Laurate (12:… 974        Body mass i…   0.418  7.10e-3
#>  9 UKB-a:124   Treatment/me… 974        Body mass i…  -5.14   1.08e-1
#> 10 95          Body mass in… 974        Body mass i…   0.981  2.79e-2
#> # … with 360 more rows, and 6 more variables: p <dbl>, ci_upp <dbl>,
#> #   ci_low <dbl>, selection <chr>, method <chr>, moescore <dbl>)

For more information on how to use the epigraphdb R package and how to use the API in R please check out the following articles:

Getting started with EpiGraphDB in R
Using EpiGraphDB R package
Using EpiGraphDB API (from R and command line)
Package options
Meta functionalities of the EpiGraphDB platform
Case study 1: Distinguishing vertical and horizontal pleiotropy for SNP-protein associations
Case study 2: Identification of potential drug targets
Case study 3: Triangulating causal estimates with literature evidence


If you would like to contribute to this package, please check out documentation on setting up development and currently planned updates.

EpiGraphDB resources

link screenshot
docs docs
API api
web application webapp
r package epigraphdb-r


Please cite EpiGraphDB as

Yi Liu, Benjamin Elsworth, Pau Erola, Valeriia Haberland, Gibran Hemani, Matt Lyon, Jie Zheng, Oliver Lloyd, Marina Vabistsevits, Tom R Gaunt, EpiGraphDB: a database and data mining platform for health data science, Bioinformatics, btaa961, https://doi.org/10.1093/bioinformatics/btaa961

    author = {Liu, Yi and Elsworth, Benjamin and Erola, Pau and Haberland, Valeriia and Hemani, Gibran and Lyon, Matt and Zheng, Jie and Lloyd, Oliver and Vabistsevits, Marina and Gaunt, Tom R},
    title = {{EpiGraphDB}: a database and data mining platform for health data science},
    journal = {Bioinformatics},
    year = {2020},
    month = {11},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa961},
    url = {https://doi.org/10.1093/bioinformatics/btaa961},
    note = {btaa961},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa961/34178613/btaa961.pdf}


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