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Implementation for CS-LMM (Constrained Sparse multi-locus Linear Mixed Model)
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CS-LMM (Constrained Sparse multi-locus Linear Mixed Model)

Implementation of CS-LMM in this paper:

''Wang H, Vanyukov MM, Xing EP, and Wu W. Discovering Genetic Variants with Weak Associations Guided by Known Variants''


CS-LMM is used to detect the weaker genetic association conditioned on the stronger validated associations.

File Structure:

  • models/ main method for CS-LMM
  • utility/ other helper files
  • main entry point of using CS-LMM to work with your own data

An Example Command:

python -n data/mice.plink

Data Support

  • CS-LMM currently supports CSV and binary PLINK files.
  • Extensions to other data format can be easily implemented through FileReader in utility/dataLoadear. Feel free to contact us for the support of other data format.


You will need to have numpy, scipy and pysnptool installed on your current system. You can install CS-LMM using pip by doing the following

   pip install git+

You can also clone the repository and do a manual install.

   git clone
   python install

Software with GUI

Software with GUI will be avaliable through GenAMap


Haohan Wang

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