jtb114/ldsc
Update of LDSC so it uses python3 instead of python2.
330
v3.0.0
ldsc
is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics. ldsc
also computes LD Scores.
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-test: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-test
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-test
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-test-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/
You can test that the image works by running the following commands to generate help messages for the programs:
ldsc.py -h
munge_sumstats.py -h
If you need to update LDSC just pull the image again using the 'latest' tag. We will try to make sure that the latest tag has the most update changes.
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 (Graduate student in the BelowLab at Vanderbilt University) Alex Petty (Vanderbilt University Medical Center)
docker pull jtb114/ldsc