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 an association in 90% of trials at a significance level set at p<5×10−8. The sample size obtained at each iteration of the simulation was specified according to a binary search that terminated once 90% power (over 10,000 replicates) was achieved, or when the sample size limit of 1,000,000 was reached. In the second set of simulations, we studied the impact of β on the statistical power to detect association at p<5×10−8. To this end we generated 10,000 cases and controls, replicated 1,000 times, for each combination of parameters. For each genetic model (dominant and multiplicative) we obtained case-control frequency tables. Using χ2 and the Cochran-Armitage-Trend-Test (CATT) module implemented in R we calculated p-values and odds ratios (ORs) for increasing levels of β.
