ArticleJournal of the American Statistical Association2022
Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
57 citing papers in PubMed, 91 citations in OpenAlex.
- Differential DNA methylation in blood as potential mediator of the association between ambient PMMolecular psychiatry · 2026Article
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- Proteomic profiling reveals immunologic mediators of endotoxin-related lung function decline: a longitudinal prospective study in textile workers.Respiratory research · 2026Article
- MELODY: Mediation Analysis in Logistic Regression for High-Dimensional Mediators and a Binary Outcome.Genetic epidemiology · 2026Article
- Powerful large scale inference in high dimensional mediation analysis.PLoS computational biology · 2026Article
- Methods for analysing wildlife DNA methylation data.Conservation physiology · 2026Article
- Microbiome mediation analysis: methods, assumptions, and practical considerations.Frontiers in cellular and infection microbiology · 2026Review
- Mediation with External Summary Statistic Information.Biostatistics (Oxford, England) · 2025Article
- Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.Biostatistics (Oxford, England) · 2025Article
- MELODY: Mediation Analysis in Logistic Regression for High-Dimensional Mediators and a Binary Outcome.bioRxiv : the preprint server for biology · 2025Article
- HILAMA: High-dimensional multi-omics mediation analysis with latent confounding.BMC medical research methodology · 2025Article
- High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application.Bioinformatics (Oxford, England) · 2025Article
- Large-scale composite hypothesis testing procedure for omics data analyses.NAR genomics and bioinformatics · 2025Article
- Placental DNA methylation key topics: sex- and cell-type specificity, mediation, multi-omics, and biomarker discovery.Epigenomics · 2025Review
- Debiased machine learning for ultra-high dimensional mediation analysis.Bioinformatics (Oxford, England) · 2025Article
- A Scoping Review of Epigenetic Signatures of Diet and Diet-related Metabolites: Insights from Epigenome-Wide Association Studies and Their Implications for Cardiometabolic Health and Diseases.The Yale journal of biology and medicine · 2025Article
- Post-selection inference for high-dimensional mediation analysis with survival outcomes.Scandinavian journal of statistics, theory and applications · 2025Article
- Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025Article
- Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies.Journal of the American Statistical Association · 2025Article
- Joint mirror procedure: controlling false discovery rate for identifying simultaneous signals.Biometrics · 2024Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
Abstract
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.
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