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 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:

1. The wiki has tutorials on estimating LD Score, heritability, genetic correlation and the LD Score regression intercept and partitioned heritability.
2. Common issues are described in the FAQ
3. 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 [email protected]

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: https://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: https://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: https://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)