RT Journal Article
SR Electronic
T1 A Bayesian model selection approach to mediation analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.07.19.452969
DO 10.1101/2021.07.19.452969
A1 Wesley L Crouse
A1 Gregory R Keele
A1 Madeleine S Gastonguay
A1 Gary A Churchill
A1 William Valdar
YR 2021
UL http://biorxiv.org/content/early/2021/07/20/2021.07.19.452969.1.abstract
AB Mediation analysis is a powerful tool for discovery of causal relationships. We describe a Bayesian model selection approach to mediation analysis that is implemented in our bmediatR software. Using simulations, we show that bmediatR performs as well or better than established methods including the Sobel test, while allowing greater flexibility in both model specification and in the types of inference that are possible. We applied bmediatR to genetic data from mice and human cell lines to demonstrate its ability to derive biologically meaningful findings. The Bayesian model selection framework is extensible to support a wide variety of mediation models.Competing Interest StatementThe authors have declared no competing interest.