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.

中文翻译：

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评估暴露对基因表达的影响。

在观察性基因组学数据集中，暴露对基因表达的影响通常会混淆。为了在估计暴露效应时调整混杂因素，一种常见的方法包括在基因表达的回归模型中将潜在混杂因素作为暴露的协变量包括在内。然而，当暴露和混杂因素相互作用影响基因表达时，拟合回归模型不一定估计暴露的整体影响。在这些情况下使用逆概率加权 (IPW) 或参数 g 公式很容易应用并产生一致的效果估计。IPW 可以通过上游数据处理和归一化轻松集成到基因组数据分析管道中，而 g 公式可以通过对回归模型进行简单修改来实现。本文使用模拟比较了回归、IPW 和 g 公式方法来估计暴露效应；探讨了每种方法的优缺点。这些方法应用于一个案例研究，估计当前吸烟对脂肪组织中基因表达的影响。