d-ldsc

Dominance extension to the LD-score regression framework.

d-ldsc is a command line tool to determine the non-additive contribution of site specific effects to heritability of a trait. This code started as a fork of the ldsc codebase, but has subsequently diverged. However, much of their excellent tutorials and gotchas still apply, so be sure to check out the orginal ldsc wiki. d-ldsc builds on first LD-score regression paper, see the citation below. Also, much of this README is based on the original ldsc README.

d-ldsc also computes additive and dominance LD Scores, and has functionality to create phenotypes (with a specified polygenic underlying architecture comprising an additive and dominance contributions), and summary statistics (additive and dominance marginal associations).

The code is slightly different to use than ldsc. Rather than a single command with a collection of flags to do most of the work, we have split the executables into a collection of files to perform distinct tasks. They are:

• get_y.py
• get_sumstats.py
• get_ldscores.py
• get_h2.py

These tools allow us to simulate polygenic continuous phenotypes with infinitesimal and spike/slab genetic architecture for additive and dominance heritability, evaluate the associated summary statistcs, determine additive and dominance LD-score in a reference panel, and determine estimates of the additive and dominance heritability, respectively.

Run the -h for with each of the commands for detailed information about the various option flags.

Getting Started

In order to download d-ldsc, you should clone this repository via the commands

git clone https://github.com/astheeggeggs/d-ldsc.git
cd d-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 d-ldsc

Once the above has completed, you can run:

./get_y.py -h
./get_sumstats.py -h
./get_ldscores.py -h
./get_h2.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.

Updating d-ldsc

You can update to the newest version of d-ldsc using git in the usual way. Navigate to your d-ldsc/ directory (e.g., cd ldsc), then run

git pull

If d-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/astheeggeggs/d-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 d-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 d-ldsc environment using

conda env update --file environment.yml

Support

Before contacting us, please try the following:

1. The original ldsc github has an excellent wiki which contains tutorials on estimating LD Score, [heritability]. Much of these tutorials are applicable to usage of d-ldsc, you can also consult the orginal FAQ.
2. The method to estimate additive heritability is described in the citation below. We have preprint incoming detailing estimation of dominance heritability in the UK-biobank, which also includes the method to estimate dominance heritability. A working version of the methods portion of supplement to our preprint is available in this repository and gives a detailed derivation of the approach. You can find it here.

If that doesn't work, you can get in touch with us by submitting an issue to this repository.

Citation

If you use the dominance LD-score regression software please cite this github repository and

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

License

This project is licensed under GNU GPL v3.

Authors

Duncan Palmer (MGH and Broad Institute of MIT and Harvard)

Original ldsc codebase authors: Brendan Bulik-Sullivan (Broad Institute of MIT and Harvard) Hilary Finucane (MIT Department of Mathematics)