The past few years have seen tremendous progress in our understanding of the genetics underlying complex disease, with associated variants being identified in dozens of traits. Despite the fact that this growing body of empirical evidence unequivocally shows the necessity for extreme levels of significance and large samples sizes, the reasoning behind these requirements is not always appreciated. As genome-wide association studies reach the limits of their resolution in the search for rarer and weaker effects, the need for appropriate design and interpretation will become ever more important. If the genetic analysis of complex disease is to avoid accumulating false positive claims, as it has in the past, then researchers will need to allow for less tangible variables such as power and prior odds rather than relying exclusively on significance when assessing the results of these studies. In this review, the basic foundations of association testing are explained from a Bayesian perspective and the potential benefits of Bayes factors as a means of measuring the weight of evidence in support of an association are described.
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