1 Introduction In genomics research, it is customary that a primary study is followed by an inde- pendent study. Reporting results from the primary study, and then reporting the evidence from the follow-up study that supports these results, gives a sense of the replicability of the results. For example, findings are informally regarded as replicated if the p -value for testing a null hypothesis is small in the primary study, and then for the same hypothesis the p -value is fairly small in the follow-up study. Many approaches are available for analyzing two or more studies, where the follow-up studies simply serve to add power. See Hedges and Olkin (1985), Benjamini and Yekutieli (2005), Skol et al. (2006), and Zeggini et al. (2007), among others. In this work, we focus on analyzing two studies, where the follow-up study serves to confirm the find- ings that were identified in the primary study. A formal statistical approach is pro- posed for evaluating whether results from a primary study were indeed replicated in a follow-up study. In observational studies, an association may fail to replicate because the discovered as- sociation was not the actual effect of a treatment but rather that of bias (Rosenbaum, 2001). However, if the finding is replicated in a different cohort, using different diag- nostic or laboratory methods, then the association between effect and outcome may be more convincingly causal. Rosenbaum (2001) gives the example of radiation and leukemia. Suppose higher rates of leukemia are discovered in a primary study among radiologists, and in a follow-up study among survivors at Hiroshima and Nagasaki. Radiation is more convincingly causal if the association discovered was replicated in the follow-up study, since if radiation was not a cause of leukemia, then higher rates of leukemia among radiologists would not lead us to expect higher rates of leukemia among survivors at Hiroshima and Nagasaki. Another example comes from the field of 1