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IJE Advance Access published online on February 11, 2008
International Journal of Epidemiology, doi:10.1093/ije/dym257
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2008; all rights reserved.

Reporting and interpretation in genome-wide association studies

Jon Wakefield

Departments of Statistics and Biostatistics, University of Washington, Seattle, USA. E-mail: jonno{at}u.washington.edu


  Abstract

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 implemented. 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 information and when used in addition to the P-value may be used to reduce the number of reported findings that are subsequently not reproduced.

Keywords Bayes theorem, epidemiologic methods, genetic polymorphism, testing

Accepted 4 December 2007


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