The question of what strength of evidence should be considered significant has yet to be fully resolved in genetic association analysis. On the one hand, multiple testing issues arise in most studies, whether based on candidate genes or genomewide scans, with attendant issues of how to quantify the multiplicity, what error rate to control and which method to use [Manly et al., 2004]. On the other hand, even under strong control of the type–1 error, many associations have not been replicated and are thought to have been false positives. This disappointing aspect can be attributed to the use of traditional thresholds of significance, even after adjustment for multiple testing, which reflect over–optimistic prior belief in the tested hypotheses [Ioannidis, 2005]. In the current period of genomewide association scans, using a dense but incomplete panel of single nucleotide polymorphism (SNP) markers [Barrett and Cardon, 2006], such considerations take on greater importance owing to the high profile and cost of these studies.
