Another approach to cumulative incorporation of more datasets stems from the frequentist methods that try to correct levels of statistical significance for sequential designs, e.g. alpha-spending functions. These approaches are routine in large clinical trials, where it is widely appreciated that the required p-value threshold should depend on how many times and when the data are analyzed, as they are sequentially assembled. In the GWA setting, one would similarly argue that if several updates in the meta-analysis are performed, more stringent p-values should be required to claim genome-wide significance as new data are introduced. While the concept is valid and methods exist for such adjustments [38], they have not yet been applied to GWA meta-analyses.
