FaST-LMM (Factored Spectrally Transformed
Linear Mixed Models) is a
set of tools for performing genome-wide association studies (GWAS) on large
data sets. FaST-LMM runs on both Windows and Linux, and has been tested
on data sets with over 120,000 individuals.
The new Python version supports single-SNP testing  including the improvements described in , as well as
SNP-set testing  and tests for epistasis. The basic functionality  is supported in the C++ versons.
Epigenome-wide association studies  are supported in FaST-LMM-EWASher.
The documentation for the Python version is:
C. Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, D. Heckerman.
FaST linear mixed models for genome-wide association studies.
Nature Methods, 8: 833-835, Oct 2011 (doi:10.1038/nmeth.1681).
J. Zou, C. Lippert, D. Heckerman, M. Aryee, J. Listgarten.
Epigenome-wide association studies without the need for cell-type composition.
Nature Methods, doi:10.1038/NMETH.2815.
(FaST-LMM-EWASher is included in FaST-LMM-Py, as well as in a seperate R version)
C. Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl M. Kadie, D. Heckerman, J. Listgarten.
Greater power and computational efficiency for kernel-based association testing of sets of genetic variants .
Bioinformatics, 2014 (doi: 10.1093/bioinformatics/btu504).
C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman.
Further Improvements to Linear Mixed Models for Genome-Wide Association Studies.
Scientific Reports, 4, 6874, Nov 2014 (doi:10.1038/srep06874).
For an annotated bibliography of all FaST-LMM-related papers, go