A Bayesian model selection approach to mediation analysis

Genetic studies often seek to establish a causal chain of events originating from genetic variation through to molecular and clinical phenotypes. When multiple phenotypes share a common genetic association, one phenotype may act as an intermediate for the genetic effects on the other. Alternatively, the phenotypes may be causally unrelated but share genetic loci. Mediation analysis represents a class of causal inference approaches used to determine which of these scenarios is most plausible. We have developed a general approach to mediation analysis based on Bayesian model selection and have implemented it in an R package, bmediatR. Bayesian model selection provides a flexible framework that can be tailored to different analyses. Our approach can incorporate prior information about the likelihood of models and the strength of causal effects. It can also accommodate multiple genetic variants or multi-state haplotypes. Our approach reports posterior probabilities that can be useful in interpreting uncertainty among competing models. We compared bmediatR with other popular methods, including the Sobel test, Mendelian randomization, and Bayesian network analysis using simulated data. We found that bmediatR performed as well or better than these alternatives in most scenarios. We applied bmediatR to proteome data from Diversity Outbred (DO) mice, a multi-parent population, and demonstrate the power of mediation with multi-state haplotypes. We also applied bmediatR to data from human cell lines to identify transcripts that are mediated through or are expressed independently from local chromatin accessibility. We demonstrate that Bayesian model selection provides a powerful and versatile approach to identify causal relationships in genetic studies using model organism or human data.Author summary: Mediation analysis is a powerful tool for the discovery of causal relationships. Causal inference is increasingly being applied to genetic and genomic data to understand the chain of intermediate steps that propagate the effects of genetic variation. We describe a Bayesian model selection approach to mediation analysis, implemented in our bmediatR software. Using simulations, we show that bmediatR performs as well or better than established causal inference methods including the Sobel test, Mendelian randomization, and Bayesian network analysis. Bayesian model selection can incorporate prior information, provides greater flexibility in model specification, and can be tailored to address different inference questions. The model selection perspective helps to delineate the assumptions of causal analyses using the Sobel test or Mendelian randomization. We applied bmediatR to data from Diversity Outbred mice and human cell lines to demonstrate its ability to derive biologically meaningful findings in a variety of contexts.