LEGIT (version 1.4.1)

rGE.LEGIT: Gene-Environment correlation estimation and testing of LEGIT models

Description

Estimates the gene-environment correlation (rGE) and tests for a GxE using a residual environmental score. If there is an important correlation between G and E, the model is still valid prediction-wise but the interpretation is affected as the question becomes: is it really a GxE or a GxG since E is partially caused by G? To account for this, we remove the influence of G on E (If E = b0 + b1*G + e, we use E_resid = E - b1*G) and refit the model to see if the model parameters changed. The residual environmental score (E_resid) is uncorrelated with G. This does not account for passive rGE but only active rGE.

Usage

# S3 method for LEGIT
rGE(object, formula, ...)

Value

Returns a list containing the Pearson correlation and Kendall tau correlation of G and E and a glm fit of the main model part when removing the influence of G on E so that E and G are now uncorrelated.

Arguments

object

An object of class "LEGIT", usually, a result of a call to LEGIT.

formula

Model formula. The names of latent_var can be used in the formula to represent the latent variables. If names(latent_var) is NULL, then L1, L2, ... can be used in formula to represent the latent variables. Do not manually code interactions, write them in the formula instead (ex: G*E1*E2 or G:E1:E2).

...

Further arguments passed to or from other methods.

Examples

# Note: These examples don't have G and E correlation so the model fit doesn't change
# but this shows how to use the rGE function
train = example_2way(500, 1, seed=777)
fit = LEGIT(train$data, train$G, train$E, y ~ G*E)
fit_rGE = rGE(fit, y ~ G*E)
fit_rGE
summary(fit_rGE$fit_main_resid)