Skip to content

Latest commit

 

History

History
304 lines (184 loc) · 9.04 KB

README.md

File metadata and controls

304 lines (184 loc) · 9.04 KB
Raw

LDSC (LD Score Regression) v2.0.0

LDSC (LD Score Regression) is a command-line tool for estimating heritability and genetic correlation from GWAS summary statistics. It also computes LD Scores. This tool is essential for researchers in genetics and genomics aiming to understand the genetic architecture of complex traits.

Table of Contents

Background

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits. However, interpreting these associations requires robust statistical tools. LDSC provides a framework to estimate the heritability of traits and the genetic correlation between them using summary statistics from GWAS, leveraging linkage disequilibrium (LD) patterns.

Scientific Foundation

LDSC implements LD Score regression, a method that distinguishes confounding biases from true polygenic signals in GWAS data. By regressing GWAS test statistics on LD Scores, LDSC estimates the proportion of variance in a trait explained by genetic factors (heritability) and assesses the shared genetic architecture between traits (genetic correlation).

Key publications:

  • Bulik-Sullivan, B., Loh, PR., Finucane, H. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291–295 (2015). https://doi.org/10.1038/ng.3211
  • Bulik-Sullivan, B., Finucane, H., Anttila, V. et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 47, 1236–1241 (2015). https://doi.org/10.1038/ng.3406
  • Finucane, H., Bulik-Sullivan, B., Gusev, A. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47, 1228–1235 (2015). https://doi.org/10.1038/ng.3404

Installation

Prerequisites

  • Python: Version >3.10
  • Git: For cloning the repository
  • Poetry: Python dependency management tool

Steps

  1. Clone the Repository

    git clone https://github.com/abdenlab/ldsc-python3.git
cd ldsc-python3

  2. Install Poetry

    If you don't have Poetry installed, you can install it using the following command:

    # From python-poetry.org
curl -sSL https://install.python-poetry.org | python3 -
# Or install with pip
pip install poetry

    Make sure to add Poetry to your PATH as instructed after installation.

  3. Install Dependencies

    Use Poetry to install all project dependencies:

    poetry install

    This will create a virtual environment and install all required packages as specified in pyproject.toml.

  4. Activate the Virtual Environment

    poetry shell

  5. Verify Installation

    Run the help command to verify that LDSC is installed correctly:

    python ldsc.py -h
python munge_sumstats.py -h

    If these commands display help messages with available options, the installation was successful.

Running LDSC

LDSC provides several functionalities, including estimating LD Scores, heritability, partitioned heritability, genetic correlation, and the LD Score regression intercept.

Estimating LD Scores

ldsc --bfile 1000G_EUR_Phase3_plink/1000G.EUR.QC. --l2 --ld-wind-cm 1 --out eur_ldscores

Estimating Heritability

ldsc --h2 sumstats.txt --ref-ld-chr eur_ldscores/ --w-ld-chr eur_weights/ --out h2_results

Estimating Genetic Correlation

ldsc --rg trait1.sumstats.gz,trait2.sumstats.gz --ref-ld-chr eur_ldscores/ --w-ld-chr eur_weights/ --out rg_results

Partitioned Heritability

ldsc --h2 sumstats.txt --ref-ld-chr eur_ldscores/ --w-ld-chr eur_weights/ --overlap-annot --frqfile-chr eur_frq/ --out partitioned_h2

Replace the file paths with your actual data files. For more detailed tutorials and options, refer to the wiki.

Testing

We have included a comprehensive test suite to ensure the correctness of LDSC.

Running Tests

  1. Activate the Virtual Environment

    poetry shell

  2. Run Tests with Nose2

    nose2

    This will execute all unit tests located in the test directory.

Continuous Integration

We use GitHub Actions for continuous integration. The workflow is defined in .github/workflows/python-project.yml and runs tests across multiple Python versions.

Contributing

We welcome contributions from the community. To contribute:

  1. Fork the Repository

  2. Create a Feature Branch

    git checkout -b feature/new-feature

  3. Make Changes and Commit

    git commit -am "Add new feature"

  4. Push to Your Fork

    git push origin feature/new-feature

  5. Create a Pull Request

Please ensure that your code passes all tests and adheres to the project's coding standards before submitting a pull request.

Coding Standards

  • Formatting: We use black for code formatting.
  • Linting: Code should pass flake8 checks.
  • Type Checking: We use mypy for static type checking.
  • Imports: Organize imports using isort.

Setting Up Development Environment

Install development dependencies:

poetry install --with dev

Docker Setup

We provide a Dockerfile to containerize the application.

Building the Docker Image

docker build -t ldsc:dev .

Running the Docker Container

docker run -it ldsc:dev ldsc -h

Adjust the command according to your needs, especially if you have specific scripts to run.

Citing LDSC

If you use LDSC in your research, please cite the following publications:

  • LD Score Regression Methodology:

    Bulik-Sullivan, B.K. et al. (2015). LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. Nature Genetics, 47(3), 291–295. doi:10.1038/ng.3211

  • Genetic Correlation:

    Bulik-Sullivan, B. et al. (2015). An Atlas of Genetic Correlations across Human Diseases and Traits. Nature Genetics, 47(11), 1236–1241. doi:10.1038/ng.3406

  • Partitioned Heritability:

    Finucane, H.K. et al. (2015). Partitioning Heritability by Functional Annotation Using Genome-Wide Association Summary Statistics. Nature Genetics, 47(11), 1228–1235. doi:10.1038/ng.3404

  • Continuous Annotation Stratified Heritability:

    Gazal, S. et al. (2017). Linkage Disequilibrium–Dependent Architecture of Human Complex Traits Shows Action of Negative Selection. Nature Genetics, 49(10), 1421–1427. doi:10.1038/ng.3954

  • Relation to Haseman-Elston Regression:

    Bulik-Sullivan, B.K. (2015). Relationship Between LD Score and Haseman-Elston Regression. bioRxiv. doi:10.1101/018283

License

This project is licensed under the GNU General Public License v3.0. You may obtain a copy of the License at https://www.gnu.org/licenses/gpl-3.0.en.html.

Authors

  • Brendan Bulik-Sullivan

    Broad Institute of MIT and Harvard

  • Hilary Finucane

    MIT Department of Mathematics

  • Thomas Reimonn

    UMass Chan Medical School

Support

If you encounter issues or have questions, please consider the following resources:

  1. Wiki and Tutorials

    Detailed tutorials are available in the wiki section.

  2. Frequently Asked Questions

    Common issues are addressed in the FAQ.

  3. Contact Us

    For further assistance, you can reach out via the GitHub Issues page.

Updating LDSC

To update to the latest version of LDSC:

  1. Navigate to the Project Directory

    cd ldsc-python3

  2. Pull the Latest Changes

    git pull

    If your local repository is up to date, you will see:

    Already up-to-date.

    Otherwise, git will display the files that have been updated.

  3. Update Dependencies

    If dependencies have changed, update them with:

    poetry install

Obtaining LD Scores

You can download precomputed LD Scores suitable for various analyses:

Partitioned LD Scores for heritability estimation are also available here.

We hope LDSC proves valuable in your research. Your contributions and feedback are greatly appreciated!