Import GWAS summary statistics from Open GWAS
Authors: Alan Murphy, Brian Schilder and Nathan Skene
Authors: Alan Murphy, Brian Schilder and Nathan Skene
Updated: Oct-26-2026
Source: Updated: Oct-26-2026
vignettes/OpenGWAS.Rmd
OpenGWAS.RmdMungeSumstats now offers high throughput query and import functionality to data from the MRC IEU Open GWAS Project.
This is made possible by the use the IEU OpwnGWAS R package:
ieugwasr.
Before you can use this functionality however, please complete the following steps:
Authenticate Access IEU OpenGWAS API
To authenticate, you need to generate a token from the OpenGWAS
website. The token behaves like a password, and it will be used to
authorise the requests you make to the OpenGWAS API. Here are the steps
to generate the token and then have ieugwasr automatically
use it for your queries:
- Login to https://api.opengwas.io/profile/
- Generate a new token
- Add
OPENGWAS_JWT=<token>to your .Renviron file, thi can be edited in R by runningusethis::edit_r_environ() - Restart your R session
- To check that your token is being recognised, run
ieugwasr::get_opengwas_jwt(). If it returns a long random string then you are authenticated. - To check that your token is working, run
ieugwasr::user(). It will make a request to the API for your user information using your token. It should return a list with your user information. If it returns an error, then your token is not working. - Make sure you have submitted user information to increasse you API limit at https://api.opengwas.io/profile/.
Find GWAS datasets
We can search by terms and with other filters like sample size:
#### Search for datasets ####
metagwas <- MungeSumstats::find_sumstats(traits = c("parkinson","alzheimer"),
min_sample_size = 1000)
head(metagwas,3)
ids <- (dplyr::arrange(metagwas, nsnp))$id ## id trait group_name year author
## 1 ieu-a-298 Alzheimer's disease public 2013 Lambert
## 2 ieu-b-2 Alzheimer's disease public 2019 Kunkle BW
## 3 ieu-a-297 Alzheimer's disease public 2013 Lambert
## consortium
## 1 IGAP
## 2 Alzheimer Disease Genetics Consortium (ADGC), European Alzheimer's Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer's Disease Consortium (GERAD/PERADES),
## 3 IGAP
## sex population unit nsnp sample_size build
## 1 Males and Females European log odds 11633 74046 HG19/GRCh37
## 2 Males and Females European NA 10528610 63926 HG19/GRCh37
## 3 Males and Females European log odds 7055882 54162 HG19/GRCh37
## category subcategory ontology mr priority pmid sd
## 1 Disease Psychiatric / neurological NA 1 1 24162737 NA
## 2 Binary Psychiatric / neurological NA 1 0 30820047 NA
## 3 Disease Psychiatric / neurological NA 1 2 24162737 NA
## note ncase
## 1 Exposure only; Effect allele frequencies are missing; forward(+) strand 25580
## 2 NA 21982
## 3 Effect allele frequencies are missing; forward(+) strand 17008
## ncontrol N
## 1 48466 74046
## 2 41944 63926
## 3 37154 54162You can also search by ID:
### By ID and sample size
metagwas <- find_sumstats(
ids = c("ieu-b-4760", "prot-a-1725", "prot-a-664"),
min_sample_size = 5000
)Import full results
You can supply import_sumstats() with a list of as many
OpenGWAS IDs as you want, but we’ll just give one to save time.
datasets <- MungeSumstats::import_sumstats(ids = "ieu-a-298",
ref_genome = "GRCH37")Summarise results
By default, import_sumstats results a named list where
the names are the Open GWAS dataset IDs and the items are the respective
paths to the formatted summary statistics.
print(datasets)## $`ieu-a-298`
## [1] "/tmp/RtmpxsovP7/ieu-a-298.tsv.gz"You can easily turn this into a data.frame as well.
results_df <- data.frame(id=names(datasets),
path=unlist(datasets))
print(results_df)## id path
## ieu-a-298 ieu-a-298 /tmp/RtmpxsovP7/ieu-a-298.tsv.gzImport full results (parallel)
Optional: Speed up with multi-threaded download via axel.
datasets <- MungeSumstats::import_sumstats(ids = ids,
vcf_download = TRUE,
download_method = "axel",
nThread = max(2,future::availableCores()-2))Further functionality
See the Getting started vignette for more information on how to use MungeSumstats and its functionality.
Session Info
utils::sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MungeSumstats_1.21.2 BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.2.1
## [3] blob_1.3.0 R.utils_2.13.0
## [5] Biostrings_2.80.2 bitops_1.1-0
## [7] fastmap_1.2.0 RCurl_1.98-1.20
## [9] VariantAnnotation_1.58.0 GenomicAlignments_1.48.0
## [11] XML_3.99-0.25 digest_0.6.39
## [13] lifecycle_1.0.5 KEGGREST_1.52.2
## [15] RSQLite_3.53.3 magrittr_2.0.5
## [17] compiler_4.6.1 rlang_1.3.0
## [19] sass_0.4.10 tools_4.6.1
## [21] yaml_2.3.12 data.table_1.18.6.1
## [23] rtracklayer_1.72.0 knitr_1.52
## [25] S4Arrays_1.12.1 bit_4.6.0
## [27] curl_8.1.0 DelayedArray_0.38.2
## [29] ieugwasr_1.2.0 abind_1.4-8
## [31] BiocParallel_1.46.0 BiocGenerics_0.58.1
## [33] desc_1.4.3 R.oo_1.27.1
## [35] grid_4.6.1 stats4_4.6.1
## [37] SummarizedExperiment_1.42.0 cli_3.6.6
## [39] rmarkdown_2.32 crayon_1.5.3
## [41] ragg_1.5.2 generics_0.1.4
## [43] otel_0.2.0 httr_1.4.9
## [45] rjson_0.2.23 DBI_1.3.0
## [47] cachem_1.1.0 stringr_1.6.0
## [49] parallel_4.6.1 AnnotationDbi_1.74.0
## [51] BiocManager_1.30.27 XVector_0.52.0
## [53] restfulr_0.0.17 matrixStats_1.5.0
## [55] vctrs_0.7.3 Matrix_1.7-5
## [57] jsonlite_2.0.0 bookdown_0.48
## [59] IRanges_2.46.0 S4Vectors_0.50.3
## [61] bit64_4.8.6 systemfonts_1.3.2
## [63] GenomicFeatures_1.64.0 jquerylib_0.1.4
## [65] glue_1.8.1 pkgdown_2.2.1
## [67] codetools_0.2-20 stringi_1.8.9
## [69] GenomeInfoDb_1.48.0 BiocIO_1.22.0
## [71] GenomicRanges_1.64.0 UCSC.utils_1.8.0
## [73] tibble_3.3.1 pillar_1.11.1
## [75] htmltools_0.5.9 Seqinfo_1.2.0
## [77] BSgenome_1.80.0 R6_2.6.1
## [79] textshaping_1.0.5 evaluate_1.0.5
## [81] lattice_0.22-9 Biobase_2.72.0
## [83] R.methodsS3_1.8.2 png_0.1-9
## [85] Rsamtools_2.28.0 cigarillo_1.2.1
## [87] memoise_2.0.1 bslib_0.12.0
## [89] SparseArray_1.12.3 xfun_0.62
## [91] fs_2.1.0 MatrixGenerics_1.24.0
## [93] pkgconfig_2.0.3