All summary statistics in this compilation are already freely available for public download and can be accessed through the other websites linked via the script gwas_downloads.sh. This link is merely a standardized aggregation of GWAS from disparate sources. I take the rights of research participants very seriously, so please don’t hesitate to contact me if you have any concerns about the security and/or privacy of these data.
By using these data, you are implicitly agreeing to follow a responsible code of conduct, as outlined, for example, at https://www.med.unc.edu/pgc/results-and-downloads/.
We feel that the potential benefits these data offer to the participants themselves, the broader scientific community, and the public far outweigh the risks for individual participants, so we encourage all investigators and research participants to be as open as possible in sharing these data. That said, we respect explicit requests of individual consortia not to distribute their data publicly; for example, we have withheld GWAS data from the PGC according to their pre-download agreement. In accordance with point (3) on the PGC website, if you contact me directly, I will privately share these restricted data. We hope that as the benefits of this research are realized, such restrictions will be lifted in the future.
For a more detailed assessment of the risks of sharing GWAS summary statistics, we recommend reading the following (short) white paper from the creators of the GWAS portal T2DKP: https://broad-portal-resources.s3.amazonaws.com/Risk_Assessment_KP_platform.pdf.
https://stanfordmedicine.box.com/s/wifpn8z0buoqozoboqhmyl1lvcdx6pfc
The files included here have been sorted, bgzipped, and tabixed, and converted to a standardized format,
as described in the munge module of this repository for compatibility with downstream analyses. You can
perform these analyses yourself by following the directions below, but this can be time-consuming.
This version is missing GWAS published after mid-2018, and you may notice errors in a few of the GWAS. I'm currently working on an updated version that will include the latest GWAS, as well as both hg19- and hg38- formatted versions of each GWAS. I'll replace this link as soon as they're ready!
This script downloads publicly available GWAS summary statistics from over 200 different publications.
No software installation is necessary; the script runs directly in a Linux terminal:
bash gwas_downloads.sh
The summary statistics files are downloaded into a separate folder for each study, with the naming convention Trait-Name_First-Author_Year.
You can download the whole collection, or you can get just a subset. The files in this script are in no particular order. You can search for a specific study either by trait name or by author name.
Some of the files are freely accessible online, but must be downloaded manually from a web browser. These files are listed in a separate section at the bottom of the script, with URLs for downloading.
When using these data, please remember to acknowledge the authors of the original studies and to cite them in any publications. Links to each GWAS publication have been included in the download script.
Help keep this list up to date by submitting pull requests with your own favorite studies. I’ll try to merge them in a timely manner.
I've uploaded the custom_munge.py script that I used to convert these GWAS summary
statistics files into a standardized format. An output (formatted) GWAS summary
statistics file contains (at minimum) the rsids, chromosome numbers along with positions
in hg19, and p-values for every tested SNP. It will also retain the other columns
from the original file, appended to the end of the table in tab-separated format.
The output file will be sorted by chromosome and position, and indexed with tabix.
The script requires a config file in JSON format with entries for each input GWAS file.
This requires a small amount of manual labor, but also takes care of a lot of common errors
that may occur in this process. Luckily, I’ve included munge_menu.config, which contains
the config specifications for the 200+ GWAS included in our original wget download script.
(Note that some of these files first need to be unzipped and may require other small formatting
tasks before munging; thus, many but not all files will be ready for processing straight
after download with the wget script. I apologize for this limitation!) For each GWAS,
the config file requires the index of either 1) the rsid column or 2) the chromosome
number and SNP position columns. It also requires the index of the p-value column.
Other options exist for GWAS files with unusual formatting, and other columns such as
effect and noneffect alleles can be included as well. See the munge_menu.config file
for a wide variety of examples.
Running custom_munge.py will also require installation of the tabix command-line
tool, as well as installation of the pandas library for Python. It also requires a
local version of dbSNP version 150 formatted for use with hg19. You can download this with
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/snp150.txt.gz
You'll then need to open the file custom_munge.py and navigate to the function
load_rsid_keys, editing the line
with gzip.open("/users/mgloud/projects/gwas/data/sorted_hg19_snp150.txt.gz") as f:
to show the location where you've stored dbSNP.
You'll also need to open munge_menu.config and modify the lines
"input_base_dir": "/mnt/lab_data/montgomery/shared/gwas/",
"output_base_dir": "/users/mgloud/projects/gwas/data/munged",
replacing these directories with the base directory where your input GWAS files are stored, and the base directory in which your formatted files should be stored.
I apologize that this script is still pretty hackish. Please contact me or post a bug report if you encounter any issues that I may be able to help you resolve.
While creating these scripts, I've found some other public tools for achieving very similar tasks. In particular, check out munge_sumstats.py from the LD Score Regression toolbox. That script performs several QC steps and sanity checks to make sure the formatted results it produces are trustworthy. I opted out of using that tool because it automates some filtering steps where I prefer to have a bit more control, which can lead to errors when using munge_sumstats.py on GWAS with unconventional formatting; however, I find it excellent for running a first pass through a large number of GWAS files.
If you know of any other tools/scripts for easily downloading and formatting GWAS, send me a comment and I'll add a link. And feel free to repurpose or modify any of the code here if it may be useful for you.
In the future, I may add:
- A script to download and index the UK Biobank rapid GWAS results from Ben Neale’s lab
- Updated results (last updated mid-2018)
- Cleaner and more functional code (it doesn't hurt to dream...)
- Mike Gloudemans
- Boxiang Liu
- Brunilda Balliu
- Abhiram Rao