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,...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-05-01
|
| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1010184 |
| _version_ | 1797957162514776064 |
|---|---|
| author | Wesley L Crouse Gregory R Keele Madeleine S Gastonguay Gary A Churchill William Valdar |
| author_facet | Wesley L Crouse Gregory R Keele Madeleine S Gastonguay Gary A Churchill William Valdar |
| author_sort | Wesley L Crouse |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T00:00:00Z |
| format | Article |
| id | doaj.art-72d0915fdb7d445a88a4acb36baf78d9 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-11T00:00:00Z |
| publishDate | 2022-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-72d0915fdb7d445a88a4acb36baf78d92023-01-10T05:31:33ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042022-05-01185e101018410.1371/journal.pgen.1010184A Bayesian model selection approach to mediation analysis.Wesley L CrouseGregory R KeeleMadeleine S GastonguayGary A ChurchillWilliam ValdarGenetic 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.https://doi.org/10.1371/journal.pgen.1010184 |
| spellingShingle | Wesley L Crouse Gregory R Keele Madeleine S Gastonguay Gary A Churchill William Valdar A Bayesian model selection approach to mediation analysis. PLoS Genetics |
| title | A Bayesian model selection approach to mediation analysis. |
| title_full | A Bayesian model selection approach to mediation analysis. |
| title_fullStr | A Bayesian model selection approach to mediation analysis. |
| title_full_unstemmed | A Bayesian model selection approach to mediation analysis. |
| title_short | A Bayesian model selection approach to mediation analysis. |
| title_sort | bayesian model selection approach to mediation analysis |
| url | https://doi.org/10.1371/journal.pgen.1010184 |
| work_keys_str_mv | AT wesleylcrouse abayesianmodelselectionapproachtomediationanalysis AT gregoryrkeele abayesianmodelselectionapproachtomediationanalysis AT madeleinesgastonguay abayesianmodelselectionapproachtomediationanalysis AT garyachurchill abayesianmodelselectionapproachtomediationanalysis AT williamvaldar abayesianmodelselectionapproachtomediationanalysis AT wesleylcrouse bayesianmodelselectionapproachtomediationanalysis AT gregoryrkeele bayesianmodelselectionapproachtomediationanalysis AT madeleinesgastonguay bayesianmodelselectionapproachtomediationanalysis AT garyachurchill bayesianmodelselectionapproachtomediationanalysis AT williamvaldar bayesianmodelselectionapproachtomediationanalysis |