|
Epigenome-wide association studies have identified associations between DNA methylation patterns and both exposures (e.g. smoking status) and certain health outcomes (e.g. BMI). These associations naturally lead to an interest in studying DNA methylation as a potential mediator between an exposure and outcome. Moreover, methylation is often measured at the bulk level, meaning samples are comprised of a mix of cell types. Distinct cell types are known to present distinct methylation profiles and play unique roles in disease pathogenesis. Failing to account for cell-type-specific effects not only ignores a confounding effect but fails to capture biologically meaningful information about specific methylation mechanisms. There is currently a lack of methods to detect cell-type-specific mediating CpG sites.
We present a novel statistical method using the EM algorithm to detect this cell-type-specific mediation effect with a continuous outcome. We use traditional mediation models and treat the unobserved cell-type-specific methylation as missing data. We then perform a bootstrap test of the indirect effect, which is the primary quantity of interest in mediation analysis.
|
