Differential susceptibility explained by interactors and mediators with different distributions

interactions

multi-ancestry

Author

Gibran Hemani

Published

June 9, 2023

Background

What leads to different effect estimates in different populations? If the DAG is the same, then different distributions of mediators and interactions could explain different effects

n <-100000g1 <-rbinom(n, 2, 0.3)g2 <-rbinom(n, 2, 0.3)# The liability for the mediator # Influenced by g (same effect in both populations)# Influenced by some other variable that gives it a different mean in the two populations# Now liability of mediator has different meansml1 <-plogis(scale(g1) +rnorm(n, 2, 1))ml2 <-plogis(scale(g2) +rnorm(n, 0, 1))hist(ml1)

hist(ml2)

# Generate mediators - they have different meansm1 <-rbinom(n, 1, ml1)m2 <-rbinom(n, 1, ml2)mean(m1)

[1] 0.81663

mean(m2)

[1] 0.49808

Disease influenced by mediator and noise (same effects)