LDSC (LD SCore) v3.0.0

ldsc is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics. ldsc also computes LD Scores.

Pulling the LDSC Image:

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

Updating LDSC:

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.

Citation:

If you use the software or the LD Score regression intercept, please cite

Bulik-Sullivan, et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. Nature Genetics, 2015.⁠

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

Gazal, S, et al. Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection. Nature Genetics, 2017.⁠

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

Zheng, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics (2016)⁠

License:

This project is licensed under GNU GPL v3.

Authors:

Brendan Bulik-Sullivan (Broad Institute of MIT and Harvard)

Hilary Finucane (MIT Department of Mathematics)

Current Maintainers of v3.0.0:

James Baker (Graduate student in the BelowLab at Vanderbilt University) Alex Petty (Vanderbilt University Medical Center)