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。


在观察基因组学数据集中，暴露对基因表达的影响经常存在混淆。为了在估计暴露效应时调整混杂因素，一种常见的方法是将潜在的混杂因素作为基因表达回归模型中暴露的协变量。然而，当暴露和混杂因素相互作用影响基因表达时，拟合的回归模型不一定能估计暴露的总体影响。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，并且可以产生一致的效果估计。 IPW 可以很容易地集成到具有上游数据处理和标准化的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的更改来实现。本文使用模拟比较了暴露效应估计的回归、IPW 和 g 公式方法；探讨了每种方法的优点和缺点。这些方法应用于估计当前吸烟对脂肪组织基因表达影响的案例研究。