GEM
GEM performs integrated epigenome-wide association analysis by combining genetic, environmental, and DNA methylation data to detect methylation quantitative trait loci (methQTLs) and genotype-by-environment (GxE) effects.
Key Features:
- R implementation: Implemented in R for epigenome-wide association studies (EWAS).
- Matrix eQTL integration: Extends the Matrix eQTL package using matrix-based iterative correlation and memory-efficient data processing.
- GEM_Emodel: GEM_Emodel analyzes associations between environmental factors and DNA methylation.
- GEM_Gmodel: GEM_Gmodel evaluates genetic influences on DNA methylation (methQTL analysis).
- GEM_GxEmodel: GEM_GxEmodel tests genotype-by-environment (GxE) interaction effects on DNA methylation.
- Efficient methQTL detection: Detects methylation quantitative trait loci (methQTLs) to link genetic variation with DNA methylation variation.
- Scalability: Optimized algorithms and large-matrix operations enable analysis of genome-wide datasets and large cohorts.
- Benchmarking and performance: Performance on publicly available datasets is attributed to optimized algorithms for large matrix operations.
- High-throughput data support: Designed to leverage high-throughput genotyping and DNA methylation detection technologies.
Scientific Applications:
- GxE and epigenetic association analysis: Investigate how genetic and environmental factors jointly influence DNA methylation patterns.
- MethQTL discovery linked to phenotypes: Identify methQTLs associated with health outcomes and disease progression.
- Genome- and population-level EWAS: Conduct epigenome-wide association studies (EWAS) at genome and population scales to inform personalized medicine and public health research.
Methodology:
Implemented in R and extending Matrix eQTL, GEM uses matrix-based iterative correlation, memory-efficient data processing, and optimized large-matrix algorithms and provides the GEM_Emodel, GEM_Gmodel, and GEM_GxEmodel functions for environmental, genetic, and GxE analyses to detect methQTLs and GxE effects.
Topics
Collections
Details
- License:
- Artistic-2.0
- Tool Type:
- command-line tool, library
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- R
- Added:
- 1/17/2017
- Last Updated:
- 12/30/2018
Operations
Data Inputs & Outputs
Gene expression QTL analysis
Publications
Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28(10):1353-1358. doi:10.1093/bioinformatics/bts163. PMID:22492648. PMCID:PMC3348564.
Pan H, Holbrook JD, Karnani N, Kwoh CK. 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. 2016;17(1). doi:10.1186/s12859-016-1161-z. PMID:27480116. PMCID:PMC4970299.
Otto C, Stadler PF, Hoffmann S. Lacking alignments? The next-generation sequencing mapper segemehl revisited. Bioinformatics. 2014;30(13):1837-1843. doi:10.1093/bioinformatics/btu146. PMID:24626854.