In the framework of the Bayes theorem, the probability that an observed association truly exists in the sampled population depends not only on the observed p-value for association, but also the power to detect the association (a function of minor allele frequency, effect size and sample size), the prior probability that the tested variant is associated with the trait under study, and the anticipated effect size. [3, 4, 12] We illustrate this in Figure 1, where we plot the Bayes Factor for association (versus no association) as a function of p-value, sample size, and minor allele frequency. [13] The Bayes Factor is the ratio of the probability of the data under the alternative hypothesis (association with the tested variant) to the probability of the data under the null hypothesis (no association). (Others define the Bayes Factor as the inverse of this ratio. [13]) The posterior odds of true association given the data are equal to the Bayes Factor times the prior odds of association. In Figure 1 the dashed line represents the Bayes Factor needed to achieve posterior odds for
