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.
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Note: The image above summarizes physiological and behavioral trait loci in CFW mice identified using GEMMA, from Parker et al, Nat. Genet., 2006.
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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".
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Fast association tests for multiple phenotypes implemented using a multivariate linear mixed model (mvLMM). In GWAS, this can correct for populations tructure and sample nonexchangeability jointly in multiple complex phenotypes.
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Bayesian sparse linear mixed model (BSLMM) for estimating PVE, phenotype prediction, and multi-marker modeling in GWAS.
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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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Copyright (C) 2012–2017, Xiang Zhou.
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