We simulated genetic data for 44,000 individuals from a pruned set of 50,335 SNPs with allele frequencies, effect estimates and Pm values drawn from the GIANT consortium. We generated an outcome trait by summing the products of the simulated allele counts and effect estimates over all SNPs for each individual, and subsequently added a randomly generated non-normal error term such that the trait resembles the observed distribution of the transformed BMI trait used in the main (real data) analyses. We also simulated a fixed binary interacting factor with 30% prevalence. Using this simulated dataset, we calculated Pm, Pv and Pint values for each SNP and undertook i) pairwise Spearman correlation analyses between Pm, Pv and Pint values (5,000 simulations), ii) enrichment analysis using binomial tests (2,500 simulations) and iii) Mann-Whitney U tests to determine systematic differences in Pv and Pm ranks (2,500 simulations). Following the same pipeline, we created additional simulated datasets narrowing down SNPs to i) those with Pm values from the lowest percentile (n = 504; highest Pm = 5×10−3) and to ii) genome-wide significant SNPs (n = 71; Pm<5×10−8), and tested the pairwise Spearman correlation for Pm, Pv and Pint values (1,000 simulations for both sets). Simulations were run using the statistical software R (v. 3.3.2).