Document Type

Article

Publication Date

5-1-2022

Publication Title

PLoS Genet

Keywords

JMG, Animals, Bayes Theorem, Causality, Mediation Analysis, Mendelian Randomization Analysis, Mice, Phenotype

JAX Source

PLoS Genet 2022 May 9; 18(5):e1010184

Volume

18

Issue

5

First Page

1010184

Last Page

1010184

ISSN

1553-7404

PMID

35533209

DOI

https://doi.org/10.1371/journal.pgen.1010184

Abstract

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.

Comments

This is an open access article distributed under the terms of the Creative Commons Attribution License.

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