Genome-wide Efficient Mixed Model Association


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 estimates variance component/chip heritability, and partitions
   it by different SNP functional categories. In particular, it uses HE
   regression or REML AI algorithm to estimate variance components when
   individual-level data are available. It uses MQS to estimate variance
   components when only summary statisics are available.

GEMMA is computationally efficient for large scale GWAS and uses freely available open-source numerical libraries.

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