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.

Key features:

1.  Fast assocation tests implemented using the univariate linear mixed model
    (LMM). In GWAS, this can correct for population structure and sample
    non-exchangeability. It also provides estimates of the proportion of
    variance in phenotypes explained by available genotypes (PVE), often called
    "chip heritability" or "SNP heritability".
2.  Fast association tests for multiple phenotypes implemented using a
    multivariate linear mixed model (mvLMM). In GWAS, this can correct for
    population structure and sample (non)exchangeability - jointly in multiple
    complex phenotypes.
3.  Bayesian sparse linear mixed model (BSLMM) for estimating PVE, phenotype
    prediction, and multi-marker modeling in GWAS.
4.  Estimation of variance components ("chip/SNP 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.
