#LDSC (LD SCore) v1.0.0

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

First, you will need to install python as well as the packages listed under the requirements header below. The easiest way to do this is with the Anaconda python distribution. All of the required packages come standard with Ananconda.

In order to download ldsc, you should clone this repository

git clone https://github.com/bulik/ldsc.git

Once you have installed ldsc as well as the required packages, typing

$ python ldsc.py -h

will print a list of all command-line options. 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 download LD Scores that are suitable for basic LD Score analyses (the LD Score regression intercept, heritability, genetic correlation) here.

##Support

Before contacting us, please try the following:

1. The tutorials have basic advice on running ldsc and interpreting the output
2. Common issues are described in the FAQ
3. The methods are described in the papers (citations below)
4. Search the issue tracker

Please report bugs on the issue tracker.

##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, et al. An Atlas of Genetic Correlations across Human Diseases and Traits. bioRxiv doi: http://dx.doi.org/10.1101/014498

For partitioned heritability, please also cite

Finucane, HK, et al. Partitioning Heritability by Functional Category using GWAS Summary Statistics. bioRxiv doi: http://dx.doi.org/10.1101/014241

##Requirements

1. Python 2.7
2. argparse 1.2.1
3. bitarray 0.8.1
4. numpy 1.8.0
5. pandas 0.15.0
6. scipy 0.10.1

##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)