FaST-LMM: FActored Spectrally Transformed Linear Mixed Models

Project Description

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 [1] including the improvements described in [4], as well as SNP-set testing [3] and tests for epistasis. The basic functionality [1] is supported in the C++ versons. Epigenome-wide association studies [2] are supported in FaST-LMM-EWASher.


The documentation for the  Python version is:

  1. 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).
  2. 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)
  3. 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).
  4. 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 here