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GMMAT (version 1.4.2)

Generalized Linear Mixed Model Association Tests

Description

Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) . For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) , including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.

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Version

Install

install.packages('GMMAT')

Monthly Downloads

2,372

Version

1.4.2

License

GPL (>= 3)

Maintainer

Han Chen

Last Published

November 17th, 2023

Functions in GMMAT (1.4.2)

glmm.score

Performing GLMM based score tests
glmm.score.meta

Performing meta-analysis for GLMM based score test results
example

Example dataset
SMMAT.meta

Meta-analysis for variant Set Mixed Model Association Tests (SMMAT)
glmm.wald

Performing GLMM based Wald tests
GMMAT-package

Generalized Linear Mixed Model Association Tests
glmmkin

Fit generalized linear mixed model with known relationship matrices
SMMAT

Variant Set Mixed Model Association Tests (SMMAT)