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# 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.
Features include:
1. 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".
2. Fast association tests for multiple phenotypes implemented using a
multivariate linear mixed model (lvLMM). In GWAS, this can correct for
populations tructure and sample nonexchangeability 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 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.
*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
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### Setup
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