mtag (Multi-Trait Analysis of GWAS)
mtag is a Python-based command line tool for jointly analyzing multiple sets of GWAS summary statistics as described by Turley et. al. (2018). It can also be used as a tool to meta-analyze GWAS results.
We recommend installing the Anaconda python distribution as it includes all of the packages listed below. It also makes updating packages relatively painless with the
conda update command.
mtag, you will need to have Python 2.7 installed with the following packages:
(Note: if you already have the Python 3 version of the Anaconda distribution installed, then you will need to create and activate a Python 2.7 environment to run
mtag. See here for details.)
mtag may be downloaded by cloning this github repository:
git clone https://github.com/omeed-maghzian/mtag.git cd mtag
To test that the tool has been successfully installed, type:
You should see a list of command-line flags and a description of the program. If an error is thrown instead, then there was some problem with the installation process.
A tutorial that walks through an example use of
mtag may be found in the wiki.
The easiest was to update
mtag is through
git. When you are in the
mtag/ directory, simply enter
which will update the
mtag files. If there have been no new updates since the last download of
mtag then the terminal will print:
We will try our best to address any problems that one may encounter when using
mtag. However, before opening an issue or emailing us, please first:
- Read the wiki, especially the tutorial and FAQ pages
- Read the desciption of the method in the paper listed below
If you use the
mtag software or methodology, please cite:
Turley, et. al. (2018) Multi-Trait analysis of genome-wide association summary statistics using MTAG. Nature Genetics doi: https://doi.org/10.1038/s41588-017-0009-4.
This project is licensed under GNU General Public License v3.
Omeed Maghzian (Harvard University, Department of Economics)
Raymond Walters (Broad Institute of MIT and Harvard)
Patrick Turley (Broad Institute of MIT and Harvard)
The development of this software was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC). This work was supported by the Ragnar Söderberg Foundation (E9/11 E42/15), the Swedish Research Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, the National Science Foundation’s Graduate Research Fellowship Program (DGE 1144083), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to NBER, R01-AG042568-02 to the University of Southern California, and 1R01MH107649-01and 1R01MH101244-02 to the Broad Institute at Harvard and MIT.