A Bayesian measure of the probability of false discovery in genetic epidemiology studies.
In light of the vast amounts of genomic data that are now being generated, we propose a new measure, the Bayesian false-discovery probability (BFDP), for assessing the noteworthiness of an observed association. BFDP shares the ease of calculation of the recently proposed false-positive report probability (FPRP) but uses more information, has a noteworthy threshold defined naturally in terms of the costs of false discovery and nondiscovery, and has a sound methodological foundation. In addition, in a multiple-testing situation, it is straightforward to estimate the expected numbers of false discoveries and false nondiscoveries. We provide an in-depth discussion of FPRP, including a comparison with the q value, and examine the empirical behavior of these measures, along with BFDP, via simulation. Finally, we use BFDP to assess the association between 131 single-nucleotide polymorphisms and lung cancer in a case-control study.
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. jonno@u.washington.edu
Jon
J
Wakefield
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D015991:Epidemiologic Measurements; D005189:False Positive Reactions; D005787:Gene Frequency; D005823:Genetics; D015810:Linkage Disequilibrium; D015233:Models, Statistical; D011336:Probability