Genome-wide Efficient Mixed Model Association (GEMMA)
GEMMA is the software implementing the Genome-wide Efficient Mixed Model Association algorithm for a standard linear mixed model and some of its close relatives for genome-wide association studies (GWAS):
- It fits a univariate linear mixed model (LMM) for marker association tests with a single phenotype to account for population stratification and sample structure, and for estimating the proportion of variance in phenotypes explained (PVE) by typed genotypes (i.e. “chip heritability”).
- It fits a multivariate linear mixed model (mvLMM) for testing marker associations with multiple phenotypes simultaneously while controlling for population stratification, and for estimating genetic correlations among complex phenotypes.
- It fits a Bayesian sparse linear mixed model (BSLMM) using Markov chain Monte Carlo (MCMC) for estimating PVE by typed genotypes, predicting phenotypes, and identifying associated markers by jointly modeling all markers while controlling for population structure.
- It is computationally efficient for large scale GWAS and uses freely available open-source numerical libraries. It is distributed under the GNU General Public License.
*above from Zhou Lab Website
To calculate a centered relatedness matrix
gemma -g mouse_hs1940.geno.txt.gz -p mouse_hs1940.pheno.txt -a mouse_hs1940.anno.txt -gk -o mouse_hs1940
perform association tests with a univariate linear mixed model
gemma -g mouse_hs1940.geno.txt.gz -eigen -p mouse_hs1940.pheno.txt -n 1 -a mouse_hs1940.anno.txt -k ./output/mouse_hs1940.cXX.txt