FaST-LMM: FActored Spectrally Transformed Linear Mixed Models

Project Description

FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) is a program for performing genome-wide association studies (GWAS) on large data sets. It runs on both Windows and Linux system, and has been tested on data sets with over 120,000 individuals [1,2].

FaST-LMM-Py extends the capabilities of FaST-LMM [1-3] using Python. These features include capabilities such as FaST-LMM-SELECT [2,3] which selects SNPs for FaST-LMM, FaST-LMM-SET [4] which handles associations between sets of variants and phenotype and FaST-LMM-EWASher[5] which performs epigenome-wide association analysis in the presence of confounders such as cell-type heterogeneity.

You can read about methods development of FaST-LMM, FaST-LMM-Select, FaST-LMM-Set and FaST-LMM-EWASher in the following papers:

  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). (*equal contributions)
  2. J. Listgarten*, C. Lippert*, C.M. Kadie, R.I. Davidson, E. Eskin, D. Heckerman*. Improved linear mixed models for genome-wide association studies. Nature Methods, 9: 525-526, June 2012 (doi:10.1038/nmeth.2037). (*equal contributions)
  3. C. Lippert*, Gerald Quon, Eun Youg Kang, Carl M. Kadie, J. Listgarten*, D. Heckerman*. The benefits of selecting phenotype-specific variants for applications of mixed models in genomics. Scientific Reports (2013) doi:10.1038/srep01815 (*equal contributions)
  4. J. Listgarten*, C. Lippert*, Eun Youg Kang, Jing Xiang, Carl M. Kadie, D. Heckerman*. A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics, 29:1526-1533, April 2013 (doi:10.1093/bioinformatics/btt177). (*equal contributions)
  5. 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)
  6. 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). (*equal contributions)

These papers are related to or use FaST-LMM and FaST-LMM-Set:

  1. J. Listgarten*, C. Lippert*, D. Heckerman*. FaST-LMM-Select for addressing confounding from spatial structure and rare variants. (*equal contributions)
  2. C. Lippert*, J. Listgarten*, Robert Davidson, Scott Baxter, Hoifung Poon, Carl M. Kadie, D. Heckerman*. An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data, Scientific Reports, 2013, doi:10.1038/srep01099 (*equal contributions)


General Info

The current release of FaST-LMM is available in three forms: Windows binary, Linux binary, and Source, and an R version of FaST-LMM-EWASher. Each downloadable form includes the license and manual


Previous releases are also available (although we recommend using the latest version):
       FaST-LMM v2.06: Windows binary, Linux binary, Source.
       FaST-LMM v2.05: Windows binary, Linux binary, Source.
       FaST-LMM v2.04: Windows binary, Linux binary, Source.
       FaST-LMM v2.03: Windows binary, Linux binary, Source.
       FaST-LMM v2.02: Windows binary, Linux binary, Source.