GEMMA: Genome-wide Efficient Mixed Model Association

GEMMA is a software toolkit for fast application of linear mixed models (LMMs) and related models to genome-wide association studies (GWAS) and other large-scale data sets.

Check out NEWS.md to see what's new in each GEMMA release.

Please post comments, feature requests or suspected bugs to Github issues. We also encourage contributions, for example, by forking the repository, making your changes to the code, and issuing a pull request.

Currently, GEMMA is supported for 64-bit Mac OS X and Linux platforms. Windows is not currently supported. If you are interested in helping to make GEMMA available on Windows platforms (e.g., by providing installation instructions for Windows, or by contributing Windows binaries) please post a note in the Github issues.

(The above image depicts physiological and behavioral trait loci identified in CFW mice using GEMMA, from Parker et al, Nature Genetics, 2016.)

Key features

Fast assocation tests implemented using the univariate linear mixed model (LMM). In GWAS, this can correct for population structure and sample nonexchangeability. It also provides estimates of the proportion of variance in phenotypes explained by available genotypes (PVE), often called "chip heritability" or "SNP heritability".
Fast association tests for multiple phenotypes implemented using a multivariate linear mixed model (mvLMM). In GWAS, this can correct for populations tructure and sample nonexchangeability jointly in multiple complex phenotypes.
Bayesian sparse linear mixed model (BSLMM) for estimating PVE, phenotype prediction, and multi-marker modeling in GWAS.
Estimation of variance components ("chip heritability") partitioned by different SNP functional categories from raw (individual-level) data or summary data. For raw data, HE regression or the REML AI algorithm can be used to estimate variance components when individual-level data are available. For summary data, GEMMA uses the MQS algorithm to estimate variance components.

Quick start

Download and install the software. See INSTALL.md.
Work through the demo. Give more details here.
Read the manual and run gemma -h. Give more details here.

Citing GEMMA

If you use GEMMA for published work, please cite our paper:

Xiang Zhou and Matthew Stephens (2012). Genome-wide efficient mixed-model analysis for association studies. Nature Genetics 44, 821–824.

If you use the multivariate linear mixed model (mvLMM) in your research, please cite:

Xiang Zhou and Matthew Stephens (2014). Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods 11, 407–409.

If you use the Bayesian sparse linear mixed model (BSLMM), please cite:

Xiang Zhou, Peter Carbonetto and Matthew Stephens (2013). Polygenic modeling with bayesian sparse linear mixed models. PLoS Genetics 9, e1003264.

And if you use of the variance component estimation using summary statistics, please cite:

Xiang Zhou (2016). A unified framework for variance component estimation with summary statistics in genome-wide association studies. Annals of Applied Statistics, in press.

License

The GEMMA source code repository is free software: you can redistribute it under the terms of the GNU General Public License. All the files in this project are part of GEMMA. This project 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 file LICENSE for the full text of the license.

The source code for the shUnit2 unit testing framework, included in this repository here, is distributed under the GNU Lesser General Public License, either version 2.1 of the License, or (at your option) any later revision.

The source code for the included Catch unit testing framework is distributed under the Boost Software Licence version 1.

What's included

This is the current structure of the GEMMA source repository:

├── LICENSE
├── Makefile
├── NEWS.md
├── README.md
├── bin
├── doc
├── example
└── src

Write a paragraph here briefly explaining what is in each of the subfolders; see Wilson et al "Good Enough Practices" paper for example of this.

Setup

To install GEMMA you can

Download the precompiled binaries (64-bit Linux and Mac only), see latest stable release.
Use existing package managers, see INSTALL.md.
Compile GEMMA from source, see INSTALL.md.

Compiling from source takes more work, but can boost performance of GEMMA when using specialized C++ compilers and numerical libraries.

Source code and latest stable release are available from the Github repository.

Precompiled binaries

Fetch the latest stable release and download the file appropriate for your platform: gemma.linux.gz for Linux, or gemma.macosx.gz for Mac OS X.
Run gunzip gemma.linux.gz or gunzip gemma.linux.gz to unpack the file.
Downloadable binaries are linked to static versions of the GSL, LAPACK and BLAS libraries. There is no need to install these libraries.

Building from source

Note that GEMMA currently does not work with GSL 2.x. We recommend linking to the latest version of GSL 1.x, which is GSL 1.16 as of this writing.

More information on source code, dependencies and installation can be found in INSTALL.md.

Credits

The GEMMA software was developed by:

Xiang Zhou
Dept. of Biostatistics
University of Michigan
2012-2017

Peter Carbonetto, Tim Flutre, Matthew Stephens, Pjotr Prins and others have also contributed to the development of this software.

tensorstrings/GEMMA