REGEM (RE-analysis of GEM summary statistics) is a software program for re-analyzing large-scale gene-environment interaction testing results, including multi-exposure interaction, joint, and marginal tests. It uses results directly from GEM output.
Current version: 1.1
- C++ compiler with C++11 support
- Boost C++ Libraries (Versions 1.70.0 - 1.79.0)
- Intel Math Kernal Library (MKL)
To install REGEM, run the following lines of code:
git clone https://github.com/large-scale-gxe-methods/REGEM
cd REGEM
cd src
make
Once REGEM is compiled, the executable ./REGEM can be used to run the program.
For a list of options, use ./REGEM --help.
List of Options
General Options:
--help
Prints available options and exits.
--version
Prints the version of REGEM and exits.
File Options:
--input-file
Path to the input file containing GEM results.
--out
Full path and extension to where REGEM output results.
Default: regem.out
--output-style
Modifies the output of REGEM. Must be one of the following:
minimum: Output the summary statistics for only the GxE and marginal G terms.
meta: 'minimum' output plus additional fields for the main G and any GxCovariate terms.
For a robust analysis, additional columns for the model-based summary statistics will be included.
full: 'meta' output plus additional fields needed for re-analyses of a subset of interactions.
Default: full
Input File Options:
--exposure-names
One or more column names in the input file naming the exposure(s) to be included in interaction tests.
--int-covar-names
Any column names in the input file naming the covariate(s) for which interactions should be included for adjustment (mutually exclusive with --exposure-names).
REGEM takes as input an output file from a GEM run with the --output-style flag set to "full". The --output-style flag is available in GEMv1.4.1 and later versions of the software.
REGEM will write results to the output file specified with the --out parameter (or 'regem.out' if no output file is specified). Below are details of the possible column headers in the output file.
SNPID - The SNP identifier as retrieved from the genotype file.
CHR - The chromosome of the SNP.
POS - The physical position of the SNP.
Non_Effect_Allele - The allele not counted in association testing.
Effect_Allele - The allele that is counted in association testing.
N_Samples - The number of samples without missing genotypes.
AF - The allele frequency of the effect allele.
N_catE_* - The number of non-missing samples in each combination of strata for all of the categorical exposures and interaction covariates.
AF_catE_* - The allele frequency of the effect allele for each combination of strata for all of the catgorical exposure or interaction covariate.
Beta_Marginal - The coefficient estimate for the marginal genetic effect (i.e., from a model with no interaction terms).
SE_Beta_Marginal - The model-based SE associated with the marginal genetic effect estimate.
robust_SE_Beta_Marginal - The robust SE associated with the marginal genetic effect estimate.
Beta_G - The coefficient estimate for the genetic main effect (G).
Beta_G-* - The coefficient estimate for the interaction or interaction covariate terms.
SE_Beta_G - Model-based SE associated with the the genetic main effect (G).
SE_Beta_G-* - Model-based SE associated with any GxE or interaction covariate terms.
Cov_Beta_G_G-* - Model-based covariance between the genetic main effect (G) and any GxE or interaction covariate terms.
Cov_Beta_G-*_G-* - Model-based covariance between any GxE or interaction covariate terms.
robust_SE_Beta_G - Robust SE associated with the the genetic main effect (G).
robust_SE_Beta_G-* - Robust SE associated with any GxE or interaction covariate terms.
robust_Cov_Beta_G_G-* - Robust covariance between the genetic main effect (G) and any GxE or interaction covariate terms.
robust_Cov_Beta_G-*_G-* - Robust covariance between any GxE or interaction covariate terms.
P_Value_Marginal - Marginal genetic effect p-value from model-based SE.
P_Value_Interaction - Interaction effect p-value (K degrees of freedom test of interaction effect) from model-based SE. (K is number of major exposures)
P_Value_Joint - Joint test p-value (K+1 degrees of freedom test of genetic and interaction effect) from model-based SE.
robust_P_Value_Marginal - Marginal genetic effect p-value from robust SE.
robust_P_Value_Interaction - Interaction effect p-value from robust SE.
robust_P_Value_Joint - Joint test p-value (K+1 degrees of freedom test of genetic and interaction effect) from robust SE../REGEM --input-file gem.out --exposure-names cov1 --out regem_cov1.out
For comments, suggestions, bug reports and questions, please contact Han Chen ([email protected]), Alisa Manning ([email protected]), or Kenny Westerman ([email protected]). For bug reports, please include an example to reproduce the problem without having to access your confidential data.
If you use REGEM, please cite
- Pham DT, Westerman KE, Pan C, Chen L, Srinivasan S, Isganaitis E, Vajravelu ME, Bacha F, Chernausek S, Gubitosi-Klug R, Divers J, Pihoker C, Marcovina SM, Manning AK, Chen H. (2023) Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies. Bioinformatics 39(12):btad730. PubMed PMID: 38039147. PMCID: PMC10724851. DOI: 10.1093/bioinformatics/btad730.
REGEM: RE-analysis of GEM summary statistics
Copyright (C) 2021-2024 Duy T. Pham and Han Chen
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it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
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