Mixed-model Association tests for GEne-Environment interactions (Please note: the maintained version is now located at large-scale-gxe-methods/MAGEE. Please refer to large-scale-gxe-methods/MAGEE for updated version of MAGEE after Sep 15, 2021). A CRAN release is now available at CRAN/MAGEE.
MAGEE is an R package for gene-environment interaction (GEI) tests and joint tests (testing the marginal genetic effects and GEI effects simultaneously) for genome-wide association studies (GWAS) and large-scale sequencing studies Wang et al. (2020). Based on the generalized linear mixed models (GLMMs) Breslow and Clayton (1993), the tests within the MAGEE framework are highly efficient.
For GWAS, MAGEE performs single variant test score tests for GEI and joint effects. For rare variant analysis, MAGEE performs group tests based on user-defined variant sets. The group-based tests include two GEI tests and three joint tests: interaction variance component test (IV), interaction hybrid test using Fisher's method (IF), joint variance component test (JV), joint hybrid test using Fisher's method (JF), and joint hybrid test using double Fisher's procedures (JD). Before running MAGEE for analyzing the data across the whole genome, a global null model that only accounts for covariates (not including genetic main effects) is fitted. The model should be fitted using the R package GMMAT proposed by Chen et al. (2016). See the vignettes for details.
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 1.0.0 (May 1, 2021).
This software is licensed under GPL-3.
Please refer to the R help document of MAGEE for specific questions about each function. For comments, suggestions, bug reports and questions, please contact Xinyu Wang (xinyu.wang AT uth.tmc.edu). For bug reports, please include an example to reproduce the problem without having to access your confidential data.
This work was supported by National Institutes of Health (NIH) grants R00 HL130593 and R01 HL145025.
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