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gwsem

The goal of gwsem is to provide users with the opportunity to analyze the complex, interconnected array of risk factors, biomarkers, environmental antecedents, comorbid disorders, and other health outcomes on a genome-wide basis using structural equation modeling techniques.

At the moment, plink formats are not supported on the ARM64 architecture. This shortcoming is likely easy to cure once we get access to ARM64 hardware for testing.

Installation

GW-SEM utilizes the optimization function of OpenMx.

You can install the released version of OpenMx from CRAN with:

install.packages("OpenMx")

If you want a new version of OpenMx, you can follow the instruction to build it from source HERE.

You can install the released version of gwsem from CRAN with:

install.packages("gwsem")

GW-SEM is currently under development. Therefore, the CRAN version of the software may not include the latest modifactions, enhancements, or functionality.

If you want to use a development snapshot, clone the source code and do

git clone https://github.com/jpritikin/gwsem
cd gwsem
./tools/rox
R CMD INSTALL .
Rscript tools/test.R

You cannot use devtools install_github because it does not run roxygen2.

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Version

Install

install.packages('gwsem')

Monthly Downloads

160

Version

2.1.4

License

GPL (>= 3)

Issues

Pull Requests

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Maintainer

Last Published

January 18th, 2022

Functions in gwsem (2.1.4)

buildAnalysesPlan

Build a plan for data analyses
prepareComputePlan

Return a suitable compute plan for a genome-wide association study
buildOneFacRes

Build a model suitable for a single factor residual genome-wide association study
buildTwoFac

Build a model suitable for a two factor genome-wide association study
signifGxE

Compute Z score and p-value for parameter of focus at particular moderator level
setupExogenousCovariates

Set up exogenous model covariates
gwsem-package

Genome-wide Structural Equation Modeling
plot.gwsemResult

Creates a Manhattan plot
numAvailableRecords

Probe the number of available records
isSuspicious

Determine which results are suspicious
loadSuspicious

Load suspicious GWAS results into a single data.frame
loadResults

Load GWAS results into a single data.frame
setupThresholds

Set up thresholds for ordinal indicators
signif

Compute Z score and p-value for parameter of focus
buildItem

Build a model suitable for a single item genome-wide association study
buildOneFac

Build a model suitable for a single factor genome-wide association study
GWAS

Run a genome-wide association study (GWAS) using the provided model