Note: This is standalone version of mtSet, which was used to produce all the results as shown in the manuscript. mtSet will be available in LIMIX software suite (http://pypi.python.org/pypi/limix) within the next few days.

Set tests are a powerful approach for genome-wide association testing between groups of genetic variants and individual quantitative traits. mtSet is an implementation of efficient set test algorithms for joint analysis across multiple traits. mtSet can account for confounding factors such as relatedness and can be used for analysis of single and multiple traits.

The implementation is in Python and builds on the Gaussian process toolbox in pygp (https://github.com/PMBio/pygp).

By Francesco Paolo Casale (casale@ebi.ac.uk), Barbara Rakitsch (rakitsch@ebi.ac.uk) and Oliver Stegle (stegle@ebi.ac.uk)

mtSet requires Python 2.7 with scipy, h5py, numpy, pylab.

mtSet can use Plink 1.9 to calculate the genetic relatedness matrix from bed genotype files. If Plink 1.9 is not found, a python implementation is used. We strongly recommend using Plink 1.9 for large genotype files.

The repository can be cloned using git

git clone https://github.com/PMBio/mtSet.git

mtSet can be run from the command line using the scripts in mtSet/bin as standalone software as shown in the tutorial.

mtSet has been tested on Mac Os X and Linux. If you have any trouble installing it, please contact us.

Please find instructions and examples in the tutorial.

See [LICENSE] https://github.com/PMBio/mtSet/blob/master/LICENSE