Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12859-016-1161-z
Title: Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment
Authors: Pan Hong
Holbrook Jonna D.
Karnani Neerja 
Kwoh Chee Keong
Issue Date: 2-Aug-2016
Publisher: BioMed Central Ltd.
Citation: Pan Hong, Holbrook Jonna D., Karnani Neerja, Kwoh Chee Keong (2016-08-02). Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment. BMC bioinformatics 17 (1) : Article number 299. ScholarBank@NUS Repository. https://doi.org/10.1186/s12859-016-1161-z
Abstract: 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/. © 2016 The Author(s).
Source Title: BMC bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/135339
ISSN: 14712105
DOI: 10.1186/s12859-016-1161-z
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
2016-gene-environment_methylation-tool_suite-pub.pdf2.39 MBAdobe PDF

OPEN

PublishedView/Download

SCOPUSTM   
Citations

12
checked on Feb 23, 2021

WEB OF SCIENCETM
Citations

12
checked on Feb 23, 2021

Page view(s)

214
checked on Feb 27, 2021

Download(s)

483
checked on Feb 27, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.