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+# GEMMA: Genome-wide Efficient Mixed Model Association
+
+GEMMA is a software toolkit for fast application of linear mixed
+models and related models to genome-wide association studies (GWAS)
+and other large-scale data sets.
+
+![Genetic associations discovered in CFW mice using GEMMA (Parker et al,
+Nat. Genet., 2016)](cfw.gif)
+
+Features include:
+
++ Fast assocation tests implemented using the univariate linear mixed
+model (LMM). In GWAS, this can correct for account for population
+stratification and sample nonexchangeability. It also provides
+estimates of the proportion of variance in phenotypes explained (PVE)
+by available genotypes (often called "chip heritability" or "SNP
+heritability").
+
++ Fast association tests for multiple phenotypes implemented using a
+multivariate linear mixed model (lvLMM).
+
+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.
+
+*Add note here about posting questions, comments or bug reports to
+Issues.*
+
+### Citing GEMMA
+
+*Add text here.*
+
+### License
+
+Copyright (C) 2012–2017, Xiang Zhou.
+
+### Quick start
+
+*Add text here.*
+
+### Setup
+
+*Add text here.*
+
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