To study the impact of heterogeneity on GWAS findings, we simulated case-control data assuming increasing (from 10% to 90% in 10% steps) phenotypic admixture. Admixture (indicated as β) was the proportion of “non-cases” (i.e. controls) in the case group. Specific genetic models of disease prevalence were used in order to simulate genotypes for case and control populations. We used two basic single-locus genetic models: dominant [22] and multiplicative (or log additive) [23]. The model parameters were the following: population prevalence for the disorder (0.001, 0.01, 0.05, 0.1), minor allele frequency (MAF) (0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5), and the relative risk of the disorder for the minor allele (1.1, 1.2, 1.3, 1.5, 2, 5, 10). We used a Monte Carlo procedure to simulate cases and controls under all combinations of the parameters listed above. Genotypes were assigned using one of two probability distributions, according to the group (case or control) that each individual came from. Using a script in R software (version 2.13.2), we performed two sets of simulations. First, we determined the minimum sample size needed to detect
