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Background: The interplay among genetic, environment and epigenetic variation is not fully understood. Advances in high-throughput genotyping methods, high-density DNA methylation detection and well-characterized sample collections, enable epigenetic association studies at the genomic and population levels (EWAS). The field has extended to interrogate the interaction of environmental and genetic (GxE) influences on epigenetic variation. Also, the detection of methylation quantitative trait loci (methQTLs) and their association with health status has enhanced our knowledge of epigenetic mechanisms in disease trajectory. However analysis of this type of data brings computational challenges and there are few practical solutions to enable large scale studies in standard computational environments. Results: GEM is a highly efficient R tool suite for performing epigenome wide association studies (EWAS). GEM provides three major functions named GEM_Emodel, GEM_Gmodel and GEM_GxEmodel to study the interplay of Gene, Environment and Methylation (GEM). Within GEM, the pre-existing "Matrix eQTL" package is utilized and extended to study methylation quantitative trait loci (methQTL) and the interaction of genotype and environment (GxE) to determine DNA methylation variation, using matrix based iterative correlation and memory-efficient data analysis. Benchmarking presented here on a publicly available dataset, demonstrated that GEM can facilitate reliable genome-wide methQTL and GxE analysis on a standard laptop computer within minutes. Conclusions: The GEM package facilitates efficient EWAS study in large cohorts. It is written in R code and can be freely downloaded from Bioconductor at https://www.bioconductor.org/packages/GEM/. [ABSTRACT FROM AUTHOR]