The concept of cumulative meta-analysis [34], where many studies or datasets are sequentially incorporated in the calculations, as they become available, fits very nicely to a Bayesian framework. Previous studies form the prior belief. Estimates are updated with each new study to generate a posterior belief. Besides this simple concept, there is also a wide literature of formal Bayesian methods that can be applied in meta-analysis [35-37]. These methods require that specific priors be applied to the uncertainty parameters and one can examine the robustness of conclusions based on these priors, i.e. whether different priors may change the results perceptibly. While we need more empirical data on how these methods would perform on the GWA setting, Bayesian meta-analysis is gradually gaining ground in other biomedical fields, since it allows for a more general view of meta-analysis, of which the typically used fixed and random effects are only a special case. Bayesian estimates of summary effects usually have increased uncertainty [37], especially if more uncertainty is introduced in the prior assumptions of between-study variance, effect size, genetic model, etc. However, there is also the possibility that, in some circumstances, borrowing strength from external prior evidence may increase certainty.
