This Julia package selects the SNPs that are most informative at predicting ancestry for your data — the best Ancestry Informative Markers (AIMs). MendelAimSelection is one component of the umbrella OpenMendel project.
Note: The three OpenMendel packages (1) SnpArrays, (2) MendelSearch, and (3) MendelBase must be installed before any other OpenMendel package will run. It is easiest if these three packages are installed in the above order and before any other OpenMendel package.
Within Julia, use the package manager to install MendelAimSelection:
pkg> add https://github.com/OpenMendel/MendelAimSelection.jl
This package supports Julia v1.0+
To run this analysis package you will need to prepare a Control file and have your data files available. The Control file holds the names of your data files and any optional parameters for the analysis. Details on the general format and contents of the Control and data files can be found on the MendelBase documentation page. Descriptions of the specific options available within the MendelAimSelection analysis package are in its documentation page.
There are example data files in the "data" subfolder of each Mendel package.
To run this analysis package, first launch Julia. Then load the package with the command:
julia> using MendelAimSelection
Next, if necessary, change to the directory containing your files, for example,
julia> cd("~/path/to/data/files/")
Finally, to run the analysis using the parameters in the control file Control_file.txt use the command:
julia> AimSelection("Control_file.txt")
Note: The package is called MendelAimSelection but the analysis function is called simply AimSelection.
If you use this analysis package in your research, please cite the following reference in the resulting publications:
OPENMENDEL: a cooperative programming project for statistical genetics. Zhou H, Sinsheimer JS, Bates DM, Chu BB, German CA, Ji SS, Keys KL, Kim J, Ko S, Mosher GD, Papp JC, Sobel EM, Zhai J, Zhou JJ, Lange K. Hum Genet. 2019 Mar 26. doi: 10.1007/s00439-019-02001-z. [Epub ahead of print] PMID: 30915546
This project is supported by the National Institutes of Health under NIGMS awards R01GM053275 and R25GM103774 and NHGRI award R01HG006139.