GMMAT is an R package for performing association tests using generalized linear mixed models (GLMMs, see Breslow and Clayton (1993)) 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. See the vignette here.
GMMAT links to R packages Rcpp and RcppArmadillo, and also imports R packages Rcpp, CompQuadForm, foreach, parallel, Matrix, methods, and data.table. GMMAT requires Bioconductor packages SeqArray and SeqVarTools to work with genotype files in the GDS format. In addition, GMMAT requires testthat to run code checks during development, and doMC to run parallel computing in glmm.score and SMMAT functions for genotype files in the GDS format (however, doMC is not available on Windows and these functions will switch to a single thread). These dependencies should be installed before installing GMMAT. See Section 3.2 of the vignette.
For optimal computational performance, it is recommended to use an R version configured with the Intel Math Kernel Library (or other fast BLAS/LAPACK libraries). See the instructions on building R with Intel MKL.
The current version is 1.4.2 (November 17, 2023).
This software is licensed under GPL (>= 3).
Please refer to the R help document of GMMAT for specific questions about each function. For comments, suggestions, bug reports and questions, please contact Han Chen (Han.Chen.2 AT uth.tmc.edu). For bug reports, please include an example to reproduce the problem without having to access your confidential data.
Duy T. Pham implemented support for BGEN genotype files and SeqVarGDSClass objects. We would like to thank Dr. Chaolong Wang and Dr. Brian Cade for comments and suggestions on GMMAT and the user manual. We would also like to thank Dr. Matthew Conomos for help with the Average Information REML algorithm, Dr. Stephanie Gogarten for help with the GDS genotype format, Jennifer Brody for help with parallel computing and App development in Analysis Commons, a cloud computing platform, Arthur Gilly for supporting reordered group definition files in SMMAT.meta, and Dr. Rounak Dey for supporting imputed dosage GDS files. The GMMAT implementation is supported by NIH grant R00 HL130593, and the analysis pipeline implementation (the gmmat App) in Analysis Commons is supported by NIH grant U01 HL120393.
If you use the single-variant test in GMMAT, please cite
If you use the variant set tests SMMAT, please cite