Liu Zhonghua, Shen Jincheng, Barfield Richard, Schwartz Joel, Baccarelli Andrea A, Lin Xihong
Department of Statistics and Actuarial Science, University of Hong Kong.
Department of Population Health Sciences, University of Utah School of Medicine.
J Am Stat Assoc. 2022;117(537):67-81. doi: 10.1080/01621459.2021.1914634. Epub 2021 May 19.
In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel's test and the joint significant test using the traditional null distribution are underpowered and thus can miss important scientific discoveries. In this paper, we show that the null distribution of Sobel's test is not the standard normal distribution and the null distribution of the joint significant test is not uniform under the composite null of no mediation effect, especially in finite samples and under the singular point null case that the exposure has no effect on the mediator and the mediator has no effect on the outcome. Our results explain why these two tests are underpowered, and more importantly motivate us to develop a more powerful Divide-Aggregate Composite-null Test (DACT) for the composite null hypothesis of no mediation effect by leveraging epigenome-wide data. We adopted Efron's empirical null framework for assessing statistical significance of the DACT test. We showed analytically that the proposed DACT method had improved power, and could well control type I error rate. Our extensive simulation studies showed that, in finite samples, the DACT method properly controlled the type I error rate and outperformed Sobel's test and the joint significance test for detecting mediation effects. We applied the DACT method to the US Department of Veterans Affairs Normative Aging Study, an ongoing prospective cohort study which included men who were aged 21 to 80 years at entry. We identified multiple DNA methylation CpG sites that might mediate the effect of smoking on lung function with effect sizes ranging from -0.18 to -0.79 and false discovery rate controlled at level 0.05, including the CpG sites in the genes AHRR and F2RL3. Our sensitivity analysis found small residual correlations (less than 0.01) of the error terms between the outcome and mediator regressions, suggesting that our results are robust to unmeasured confounding factors.
在全基因组表观遗传学研究中，评估暴露因素对临床结局的影响是否通过DNA甲基化介导具有重大科学意义。然而，因果中介效应的统计推断面临挑战，因为需要在整个表观基因组中检验大量复合零假设。两种常用检验方法，即Wald型Sobel检验和使用传统零分布的联合显著性检验，功效不足，因此可能错过重要的科学发现。在本文中，我们表明，在无中介效应的复合零假设下，Sobel检验的零分布不是标准正态分布，联合显著性检验的零分布也不是均匀分布，特别是在有限样本以及暴露因素对中介变量无影响且中介变量对结局无影响的奇点零假设情况下。我们的结果解释了为什么这两种检验功效不足，更重要的是，促使我们利用全表观基因组数据，为无中介效应的复合零假设开发一种功效更强的分-总复合零检验（DACT）。我们采用Efron的经验零框架来评估DACT检验的统计显著性。我们通过分析表明，所提出的DACT方法提高了功效，并且能够很好地控制I型错误率。我们广泛的模拟研究表明，在有限样本中，DACT方法能够正确控制I型错误率，并且在检测中介效应方面优于Sobel检验和联合显著性检验。我们将DACT方法应用于美国退伍军人事务部规范老化研究，这是一项正在进行的前瞻性队列研究，入组时年龄在21至80岁的男性。我们确定了多个可能介导吸烟对肺功能影响的DNA甲基化CpG位点，效应大小范围为-0.18至-0.79，错误发现率控制在0.05水平，包括AHRR和F2RL3基因中的CpG位点。我们的敏感性分析发现结局回归和中介回归误差项之间的残余相关性较小（小于0.01），这表明我们的结果对于未测量的混杂因素具有稳健性。
