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.
||R (≥ 3.2.0)
||Rcpp, SeqArray, SeqVarTools, CompQuadForm, foreach, parallel, Matrix, methods, data.table
||Han Chen [aut, cre],
Matthew Conomos [aut],
Duy Pham [aut],
Arthor Gilly [ctb],
Robert Gentleman [ctb, cph] (Author and copyright holder of the C
Ross Ihaka [ctb, cph] (Author and copyright holder of the C function
The R Core Team [ctb, cph] (Author and copyright holder of the C
The R Foundation [cph] (Copyright holder of the C function Brent_fmin),
Eric Biggers [ctb, cph] (Author and copyright holder of included
Tino Reichardt [ctb, cph] (Author and copyright holder of threading
code used in the included Zstandard (zstd) library),
Meta Platforms, Inc. and affiliates [cph] (Copyright holder of included
Zstandard (zstd) library)
||Han Chen <han.chen.2 at uth.tmc.edu>
||GPL (≥ 3)
||See COPYRIGHTS for details.
GMMAT copyright details
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