ldsc is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics.
ldsc also computes LD Scores.
In order to download
ldsc, you should clone this repository via the commands
git clone https://github.com/bulik/ldsc.git cd ldsc
In order to install the Python dependencies, you will need the Anaconda Python distribution and package manager. After installing Anaconda, run the following commands to create an environment with LDSC's dependencies:
conda env create --file environment.yml source activate ldsc
Once the above has completed, you can run:
./ldsc.py -h ./munge_sumstats.py -h
to print a list of all command-line options. If these commands fail with an error, then something as gone wrong during the installation process.
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.
You can update to the newest version of
git. First, navigate to your
ldsc/ directory (e.g.,
cd ldsc), then run
ldsc is up to date, you will see
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 firstname.lastname@example.org
If you use the software or the LD Score regression intercept, please cite
For genetic correlation, please also cite
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
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)
This project is licensed under GNU GPL v3.
Brendan Bulik-Sullivan (Broad Institute of MIT and Harvard)
Hilary Finucane (MIT Department of Mathematics)