ldsc is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics. ldsc also computes LD Scores.
There are several ways to install ldsc (v3.0.0). Docker and Pip are preferred but the user can also install from the github repository. Each of these ways are explained below:
ldsc is available as an image on docker and has been built for amd64 and arm64. This method of installation has all required dependencies install already. This image can be pulled using the following command:
docker pull jtb114/ldsc:latest
The working directory of this image is "app". There is a subdirectory called "ldsc" that has all the scripts used by ldsc such as "ldsc.py", "munge_sumstats.py", and "make_annot.py" (You will have to mount the appropriate data directories to the images). Users can then run this image in interactive mode using the following command:
docker run -it jtb114/ldsc
Sometimes in HPC environments, other containerization software is preferred to docker. One option is Singularity. Singularity is able to pull images from Dockerhub so the following command will pull the ldsc image and build a singularity image:
singularity pull docker://jtb114/ldsc
This command will create a file called ldsc-test-latest.sif in the directory in which the command is run. Users can then run the singularity image using the following command:
singularity shell ldsc-latest.sif
This will open the container in an interactive mode. Users can find the scripts for ldsc in the root directory but running a command such as:
ls /app/ldsc/
ldsc is also on PYPI and can be installed by Pip using the following command (It is preferable that the program be installed into a virtual environment created using venv or conda):
pip install ldsc
warning: The "make_annot.py" script requires that bedtools be installed. There have been issues when running ldsc on an HPC environment when bedtools has not been detect even when loaded/installed. If this error occurs, it is required to use the docker image to run ldsc.
In order to download ldsc, you should clone this repository via the commands. This method is not recommended over Pip or Docker because the import structure of the files is configured for python packaging. Therefore, there is no guarantee that the code will run using this method.
git clone https://github.com/belowlab/ldsc.git
cd ldsc
In order to install the Python dependencies, we recommend you use a modern python dependency manager such as pdm.
Installing dependencies using PDM All of ldsc's dependencies are listed within the pyproject.toml file. If you are using ldsc to run analyzes then all the necessary dependencies can be installed using the following command:
pdm install --without devThe above command will install all of the runtime dependencies without install development dependencies such as black, nox, etc... If you are helping develop/maintain ldsc you can install all dependencies by just running:
pdm installWarning: LDSC assumes you have BedTools installed in your path. This tool is packaged within the docker container but anyone installing the code from github or pip will have to install BedTools for themselves.
after install dependencies using pdm Once the above has completed, you can run ldsc using the following command:
pdm run ldsc --help
to print a list of all ldsc subcommands. If these commands fail with an error, then something as gone wrong during the installation process.
You can also build a wheel in the home directory and install that into a custom virtualenv. The command to do this is shown below and will generate the wheel in a "dist" directory:
pdm build
If you do install from github: If ldsc is installed from github we recommend running the unit tests to ensure that the installation was successful. These unit test depend on simulated data so you first have to generate that data. The following commands will help you generate the data, run the test, and clean up afterwards:
pdm simulate
pdm test
pdm clean
Tutorials (This section needs to be updated because the command line API was broken in creating LDSC v3.0.1):
Short tutorials describing the four basic functions of ldsc (estimating LD Scores, h2 and partitioned h2, genetic correlation, the LD Score regression intercept) can be found in the wiki. If you would like to run the tests, please see the wiki.
warning: The "make_annot.py" script requires that bedtools be installed. There have been issues when running ldsc on an HPC environment when bedtools has not been detect even when loaded/installed. If this error occurs, it is required to use the docker image to run ldsc.
If using docker/singularity, you can update ldsc to a new version by by pulling the new image down. If installed from github. You can update to the newest version of ldsc using git. First, navigate to your ldsc/ directory (e.g., cd ldsc), then run
git pull
If ldsc is up to date, you will see
Already up-to-date.
otherwise, you will see git output similar to
remote: Counting objects: 3, done.
remote: Compressing objects: 100% (3/3), done.
remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0
Unpacking objects: 100% (3/3), done.
From https://github.com/bulik/ldsc
95f4db3..a6a6b18 master -> origin/master
Updating 95f4db3..a6a6b18
Fast-forward
README.md | 15 +++++++++++++++
1 file changed, 15 insertions(+)
which tells you which files were changed. If you have modified the ldsc source code, git pull may fail with an error such as error: Your local changes to the following files would be overwritten by merge:.
In case the Python dependencies have changed, you can update the LDSC environment with
conda env update --file environment.yml
You can download European and East Asian LD Scores from 1000 Genomes here. These LD Scores are suitable for basic LD Score analyses (the LD Score regression intercept, heritability, genetic correlation, cross-sex genetic correlation). You can download partitioned LD Scores for partitioned heritability estimation here.
Before contacting us, please try the following:
- The wiki has tutorials on estimating LD Score, heritability, genetic correlation and the LD Score regression intercept and partitioned heritability.
- Common issues are described in the FAQ
- The methods are described in the papers (citations below)
If that doesn't work, you can get in touch with us via the google group.
Issues with LD Hub? Email ld-hub@bristol.ac.uk
If you use the software or the LD Score regression intercept, please cite
For genetic correlation, please also cite
Bulik-Sullivan, B., et al. An Atlas of Genetic Correlations across Human Diseases and Traits. Nature Genetics, 2015. Preprint available on bioRxiv doi: http://dx.doi.org/10.1101/014498
For partitioned heritability, please also cite
Finucane, HK, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature Genetics, 2015. Preprint available on bioRxiv doi: http://dx.doi.org/10.1101/014241
For stratified heritability using continuous annotation, please also cite
If you find the fact that LD Score regression approximates HE regression to be conceptually useful, please cite
Bulik-Sullivan, Brendan. Relationship between LD Score and Haseman-Elston, bioRxiv doi: http://dx.doi.org/10.1101/018283
For LD Hub, please cite
This project is licensed under GNU GPL v3.
Brendan Bulik-Sullivan (Broad Institute of MIT and Harvard)
Hilary Finucane (MIT Department of Mathematics)
James Baker (Vanderbilt University)