Reporting and interpretation in genome-wide association studies

Background In the context of genome-wide association studies we critique a number of methods that have been suggested for flagging associations for further investigation. Methods The P-value is by far the most commonly used measure, but requires careful calibration when the a priori probability of an association is small, and discards information by not considering the power associated with each test. The q-value is a frequentist method by which the false discovery rate (FDR) may be controlled. Results We advocate the use of the Bayes factor as a summary of the information in the data with respect to the comparison of the null and alternative hypotheses, and describe a recently-proposed approach to the calculation of the Bayes factor that is easily imple-mented. The combination of data across studies is straightforward using the Bayes factor approach, as are power calculations. Conclusions The Bayes factor and the q-value provide complementary informa-tion and when used in addition to the P-value may be used to reduce the number of reported findings that are subsequently not reproduced