FaST-LMM
FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples.
This release contains the following features, each illustrated with an IPython notebook.
- Core FaST-LMM (notebook) -- Lippert et al., Nature Methods 2011
Improvements:
- New features for single_snp (including effect size and multiple phenotype support) and epistasis (including reporting beta and using pre-computed eigenvalue decompositions) (notebook) -- Lippert et al., Nature Methods 2011
- Ludicrous-Speed GWAS (notebook) -- Kadie and Heckerman, bioRxiv 2018
- Heritability with Spatial Correction (notebook), Heckerman et al., PNAS 2016
- Two Kernels (notebook) -- Widmer et al., Scientific Reports 2014
- Set Analysis (notebook) -- Lippert et al., Bioinformatics 2014
- Epistasis (notebook) -- Lippert et al., Scientific Reports, 2013
- Prediction (notebook) -- Lippert et al., Nature Methods 2011
A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.
Quick install:
pip install fastlmm
FaST-LMM supports Python 3.10 through 3.14.
If you need support for BGEN files, instead do:
pip install fastlmm[bgen]
For best performance, be sure your Python distribution includes a fast version of NumPy. We use Anaconda's Miniconda.
Documentation
- IPython Notebooks:
- Main Documentation
- Project Home and Full Annotated Bibliography
Code
Development
The project uses uv for reproducible development environments, dependency locking, testing, and builds:
uv sync --frozen --all-extras
cd tests
uv run --frozen --no-sync python test.py
cd ..
uv build --no-sources
See RELEASING.md for lower-bound, artifact, notebook, and release qualification.
Policy on AI-assisted development and contributions
AI tools may be used as productivity aids for drafting, exploration, and refactoring. Every contributed code or documentation change must be reviewed, edited, and validated by a human. AI does not replace design judgment, testing, or human responsibility for correctness.
The repository's published instructions and constraints for AI tools are in AGENTS.md.
Contacts
- Email the developers at fastlmm-dev@python.org.
- Join the user discussion and announcement list (or use web sign up).
- Open an issue on GitHub.
4 maintainers
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