There is no agreed threshold in the literature, as Wittke-Thompson, Pluzhnikov, and Cox (2005, p. 967) state, “. . . there is little consensus on the correct thresholds for identifying DHW (departure from HWE) in the context of large-scale studies” (I have added the expression in italics). In practice a range of thresholds have been used: Easton et al. (2007) use a threshold of 10−5 for breast cancer (227,876 SNPs, with 400 controls); Libioulle et al. (2007) use 10−3 for Crohn's disease (317,5497 SNPs, with 928 controls); Zeggini et al. (2007) use 10−4 for type 2 diabetes (459,448 SNPs, with 2938 controls); Stacey et al. (2007) use 10−10 (317,503 SNPs, with 11,563 controls). It would seem desirable to have a p-value threshold that decreases with increasing sample size (because in the limit we would not want to make any type I errors), but this does not seem to be exercised in practice, because power is not considered when a threshold is determined. Extending this idea suggests that the threshold should be minor allele frequency (MAF) specific also, as power is a function of the MAF. The Bayes factor approach explicitly considers power (i.e., sample size and MAF) in its calculation
