Abstract
BACKGROUND: It is widely appreciated that genomewide association studies often yield overestimates of the association of a marker with disease when attention focuses upon the marker showing the strongest relationship. For example, in a case-control setting the largest (in absolute value) estimated odds ratio has been found to typically overstate the association as measured in a second, independent set of data. The most common reason given for this observation is that the choice of the most extreme test statistic is often conditional upon first observing a significant p value associated with the marker. A second, less appreciated reason is described here. Under common circumstances it is the multiple testing of many markers and subsequent focus upon those with most extreme test statistics (i.e. highly ranked results) that leads to bias in the estimated effect sizes. CONCLUSIONS: This bias, termed ranking bias, is separate from that arising from conditioning on a significant p value and may often be a more important factor in generating bias. An analytic description of this bias, simulations demonstrating its extent, and identification of some factors leading to its exacerbation are presented.