haohanwang / cs-lmm

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

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

Implementation of CS-LMM in this paper:

Wang, H., Vanyukov, M. M., Xing, E. P., & Wu, W. (2020). Discovering weaker genetic associations guided by known associations. BMC Medical Genomics, 13(3), 1-10.

Introduction

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
• cslmm.py main entry point of using CS-LMM to work with your own data

An Example Command:

python cslmm.py -n data/mice.plink

Instructions

  Options:
  -h, --help          show this help message and exit

  Data Options:
    -t FILETYPE       choices of input file type
    -n FILENAME       name of the input file
    -v FILEVALIDATED  list of the validated markers

  Model Options:
    --lambda=LMBD     the weight of the penalizer. If neither lambda or snum
                      is given, cross validation will be run.
    --snum=SNUM       the number of targeted variables the model selects. If
                      neither lambda or snum is given, cross validation will
                      be run.
    -s                Stability selection
    -q                Run in quiet mode
    -m                Run without missing genotype imputation

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.

Python Users

Proficient python users can directly call the CMM method with python code, see example starting at Line 107

Installation (Not Required)

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+https://github.com/HaohanWang/CS-LMM

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

   git clone https://github.com/HaohanWang/CS-LMM
   python setup.py install

Contact

Haohan Wang · @HaohanWang