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+![Genetic associations discovered in CFW mice using GEMMA (Parker et al,
+Nat. Genet., 2016)](cfw.gif)
+
 # 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)
+models (LMMs) and related models to genome-wide association studies
+(GWAS) and other large-scale data sets.
 
 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.
+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.
 
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 Issues.*