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Table 2 Type I error rates of GxEsum when using binary disease traits with various population prevalence

From: GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

Scenarios Population prevalence (k) Type I error rate
Var(GxE) = 0, Var(RxE) = 0 0.025 0.052
0.05 0.042
0.1 0.076
0.5 0.054
Var(GxE) = 0, Var(RxE) = 0.1 (on the liability scale) 0.025 0.044
0.05 0.036
0.1 0.050
0.5 0.052
Average   0.050
  1. We simulated quantitative phenotypic data based on a real genotypic dataset (ARIC GWAS) including 7263 individuals with 583,085 SNPs. The phenotypes were standardised such that the mean was 0 and variance was 1, for which we applied the liability threshold model to generate affected or unaffected disease status for each individual, using various values for the population prevalence (k = 0.025, 0.05, 0.1 or 0.5). Type I error rate at a significance threshold of p-value < 0.05 was estimated from 500 replicates for each scenario and population prevalence