Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 73,690 bioRxiv papers from 320,698 authors.

Assessing exposure effects on gene expression

By Sarah A. Reifeis, Michael G. Hudgens, Mete Civelek, Karen L. Mohlke, Michael I. Love

Posted 16 Oct 2019
bioRxiv DOI: 10.1101/806554

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.

Download data

  • Downloaded 292 times
  • Download rankings, all-time:
    • Site-wide: 42,228 out of 73,690
    • In genomics: 3,693 out of 4,889
  • Year to date:
    • Site-wide: 17,488 out of 73,690
  • Since beginning of last month:
    • Site-wide: 17,488 out of 73,690

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News