Assessing exposure effects on gene expression

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Abstract
In observational genomics data sets, 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.
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Funding Information
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK093757, R01 DK118287)
  • National Heart, Lung, and Blood Institute (R21 HL135230)
  • National Institute of Allergy and Infectious Diseases (R01 AI085073)
  • National Institute of Mental Health (R01 MH118349)
  • National Institute of Environmental Health Sciences (P30 ES010126)
  • University of North Carolina at Chapel Hill (Chancellor's Fellowship)
  • National Human Genome Research Institute (R01 HG009937)

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