GMMAT
Generalized Linear Mixed Model Association Tests
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) doi:10.1016/j.ajhg.2016.02.012. 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) doi:10.1016/j.ajhg.2018.12.012, 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.
- Version1.4.2
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release11/17/2023
Documentation
Team
Han Chen
The R Core Team
Show author detailsRolesContributor, Copyright holderThe R Foundation
Show author detailsRolesCopyright holderRobert Gentleman
Show author detailsRolesContributor, Copyright holderRoss Ihaka
Show author detailsRolesContributor, Copyright holderMatthew Conomos
Show author detailsRolesAuthorDuy Pham
Show author detailsRolesAuthorArthor Gilly
Show author detailsRolesContributorEric Biggers
Show author detailsRolesContributor, Copyright holderTino Reichardt
Show author detailsRolesContributor, Copyright holderMeta Platforms, Inc. and affiliates
Show author detailsRolesCopyright holder
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- Imports5 packages
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