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Challenges and novel approaches for investigating molecular mediation

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ABSTRACT

Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block (some or all of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data.

No MeSH data available.


Schematic representation of Mendelian randomization to assess the causal effect of a molecular intermediate Mendelian randomization can be used to test the hypothesis that molecular intermediate M has a causal effect on outcome Y, given that the genetic variant G is associated with the intermediate phenotype of interest, has no association with the outcome except through the intermediate phenotype and is not related to measured or unmeasured confounding factors (C).
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ddw197-F4: Schematic representation of Mendelian randomization to assess the causal effect of a molecular intermediate Mendelian randomization can be used to test the hypothesis that molecular intermediate M has a causal effect on outcome Y, given that the genetic variant G is associated with the intermediate phenotype of interest, has no association with the outcome except through the intermediate phenotype and is not related to measured or unmeasured confounding factors (C).

Mentions: Solutions to analysing mediation which overcome unmeasured or residual confounding, reverse causation and measurement error include the use of instrumental variable methods (8), of which Mendelian randomization (MR) is a form. In MR, genetic variants robustly associated with modifiable exposures are used to infer causality (5,32,39) by serving as instrumental variables which are not associated with various confounders of the exposure-outcome association and are not directly influenced by the outcome of interest (40,41) (Fig. 4).Figure 4.


Challenges and novel approaches for investigating molecular mediation
Schematic representation of Mendelian randomization to assess the causal effect of a molecular intermediate Mendelian randomization can be used to test the hypothesis that molecular intermediate M has a causal effect on outcome Y, given that the genetic variant G is associated with the intermediate phenotype of interest, has no association with the outcome except through the intermediate phenotype and is not related to measured or unmeasured confounding factors (C).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036871&req=5

ddw197-F4: Schematic representation of Mendelian randomization to assess the causal effect of a molecular intermediate Mendelian randomization can be used to test the hypothesis that molecular intermediate M has a causal effect on outcome Y, given that the genetic variant G is associated with the intermediate phenotype of interest, has no association with the outcome except through the intermediate phenotype and is not related to measured or unmeasured confounding factors (C).
Mentions: Solutions to analysing mediation which overcome unmeasured or residual confounding, reverse causation and measurement error include the use of instrumental variable methods (8), of which Mendelian randomization (MR) is a form. In MR, genetic variants robustly associated with modifiable exposures are used to infer causality (5,32,39) by serving as instrumental variables which are not associated with various confounders of the exposure-outcome association and are not directly influenced by the outcome of interest (40,41) (Fig. 4).Figure 4.

View Article: PubMed Central - PubMed

ABSTRACT

Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block (some or all of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data.

No MeSH data available.