Computational Framework for Genome-Wide Association Studies and Meta-Studies in C/C++ with Python Interfaces
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Core Framework of easyGWAS ( for computing genome-wide association studies and meta-analysis. This is a C/C++ Framework with Python interfaces. The code includes several standard methods for performing GWAS, such as linear regression, logistic regression and popular linear mixed models (EMMAX (, FaSTLMM ( to also account for population stratification. In addition, the package contains code for the network guided multi-locus mapping method SConES (


Tutorials can be found in the Wiki section of this repository:


Code by: Dominik Gerhard Grimm Year: 2011-2016 Group: Machine Learning and Computational Biology Research Group Insitute: Max Planck Institute for Intelligent Systems, Max Planck Institute for Developmental Biology and ETH Zürich

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see