It is possible that our p-value cutoffs are either too stringent or two liberal. There is no easily achieved consensus regarding how to set these thresholds. Across all the empirical papers, we carried out in excess of 500 million statistical tests, expecting over 25 million to be significant at p < .05. Had we not published these papers as a set following prescribed procedures standardizing the analytic approach across them, readers would not easily recognize the predicament created by advancing a handful of “significant” findings in the context of so many tests. Faced with this reality, we believe we had little choice but to adopt conventional p-value cutoffs to control the familywise error rate on a per-phenotype basis, and to be cautious in the interpretation of our results. However, the thresholds we adopted in this special issue still come with the obvious cost incurred by the many false negatives buried in our data. Schumann (2014, this issue) and Patrick (2014, this issue) both made valuable suggestions regarding how to move beyond the impasse created by the burden of correcting for
