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@@ -1,39 +1,34 @@ + + # 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. - - +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. *Add note here about posting questions, comments or bug reports to Issues.* |