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FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) is a tool for large genome-wide association studies (GWAS).


GWAS study; Genotype and phenotype; Machine learning


  • Operation: Prediction and recognition
  • Software interface: Command-line user interface; Library
  • Language: Python
  • Operating system: Linux; Mac OS X; Microsoft Windows
  • License: Apache License 2.0
  • Cost: Free
  • Version name: -
  • Maturity: Mature
  • Credit: The Wellcome Trust Case Control Consortium, Wellcome Trust, US National Science Foundation, US National Institutes of Health.
  • Contact: fastlmm-dev _at_ | jennl _at_ | christoph.lippert _at_ | heckerma _at_
  • Collection: EBI Training Tools


Listgarten J, Lippert C, Kadie CM, Davidson RI, Eskin E, Heckerman D "Improved linear mixed models for genome-wide association studies." Nat. Methods 2012; 9(6):525-6
PMID: 22669648
PMCID: PMC3597090

Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D "FaST linear mixed models for genome-wide association studies" Nat Methods. 2011 Sep 4;8(10):833-5.
PMID: 21892150

Zou J, Lippert C, Heckerman D, Aryee M, Listgarten J "Epigenome-wide association studies without the need for cell-type composition" Nat Methods. 2014 Mar;11(3):309-11
PMID: 24464286

Widmer C, Lippert C, Weissbrod O, Fusi N, Kadie C, Davidson R, Listgarten J, Heckerman D "Further Improvements to Linear Mixed Models for Genome-Wide Association Studies " Sci Rep. 2014 Nov 12;4:6874.
PMID: 25387525
PMCID: PMC4230738

Heckerman D, Gurdasani D, Kadie C, Pomilla C, Carstensen T, Martin H, Ekoru K, Nsubuga RN, Ssenyomo G, Kamali A, Kaleebu P, Widmer C, Sandhu MS "Linear Mixed Model for Heritability Estimation That Explicitly Addresses Environmental Variation" Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7377-82
PMID: 27382152
PMCID: PMC4941438

Lippert C, Xiang J, Horta D, Widmer C, Kadie C, Heckerman D, Listgarten J "Greater Power and Computational Efficiency for Kernel-Based Association Testing of Sets of Genetic Variants" Bioinformatics. 2014 Nov 15;30(22):3206-14
PMID: 25075117
PMCID: PMC4221116

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