PT  - JOURNAL ARTICLE
AU  - Sarah A. Reifeis
AU  - Michael G. Hudgens
AU  - Mete Civelek
AU  - Karen L. Mohlke
AU  - Michael I. Love
TI  - Assessing exposure effects on gene expression
AID  - 10.1101/806554
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 806554
4099  - http://biorxiv.org/content/early/2019/10/16/806554.short
4100  - http://biorxiv.org/content/early/2019/10/16/806554.full
AB  - In observational genomics datasets, there is often confounding of the effect of an exposure on gene expression. To adjust for confounding when estimating the exposure effect, a common approach involves including potential confounders as covariates with the exposure in a regression model of gene expression. However, when the exposure and confounders interact to influence gene expression, the fitted regression model does not necessarily estimate the overall effect of the exposure. Using inverse probability weighting (IPW) or the parametric g-formula in these instances is straightforward to apply and yields consistent effect estimates. IPW can readily be integrated into a genomics data analysis pipeline with upstream data processing and normalization, while the g-formula can be implemented by making simple alterations to the regression model. The regression, IPW, and g-formula approaches to exposure effect estimation are compared herein using simulations; advantages and disadvantages of each approach are explored. The methods are applied to a case study estimating the effect of current smoking on gene expression in adipose tissue.