requires assumptions about effect sizes (see Methods for details).A key point from both perspectives is that interpreting the strength of evidence in an association study depends on the likely number of true associations, and the power to detect them which, in turn, depends on effect sizes and sample size. In a less-well-powered study it would be necessary to adopt more stringent thresholds to control the false-positive rate. Thus, when comparing two studies for a particular disease, with a hit with the same MAF and P value for association, the likelihood that this is a true positive will in general be greater for the study that is better powered, typically the larger study. In practice, smaller studies often employ less stringent P-value thresholds, which is precisely the opposite of what should occur. sex-differentiated test which is sensitive to associations of a different magnitude and/or direction in the two sexes.
