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/.
pip install fastlmm
For best performance, be sure your Python distribution includes a fast version of NumPy. We use Anaconda's Miniconda.
- IPython Notebooks:
- Main Documentation
- Project Home and Full Annotated Bibliography
- 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.
