Evidence map›Paper›PMID 35989709›Full record

ArticleJournal of the American Statistical Association2022

Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies.

Zhonghua Liu, Jincheng Shen, Richard Barfield, Joel Schwartz, Andrea A Baccarelli, Xihong Lin

Open access · greenAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
5.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 91 citations in OpenAlex.

  1. Article
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  7. Microbiome mediation analysis: methods, assumptions, and practical considerations.Frontiers in cellular and infection microbiology · 2026
    Review
  8. Mediation with External Summary Statistic Information.Biostatistics (Oxford, England) · 2025
    Article
  9. Article
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  13. Article
  14. Review
  15. Article
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  17. Post-selection inference for high-dimensional mediation analysis with survival outcomes.Scandinavian journal of statistics, theory and applications · 2025
    Article
  18. Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 2 countries.

Zhonghua LiuDepartment of Statistics and Actuarial Science, University of Hong Kong.
Jincheng ShenDepartment of Population Health Sciences, University of Utah School of Medicine.
Richard BarfieldDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine.
Joel SchwartzEnvironmental Epidemiology, Harvard T.H. Chan School of Public Health.
Andrea A BaccarelliEnvironmental Health Sciences, Mailman School of Public Health, Columbia University.
Xihong LinBiostatistics at Harvard T.H. Chan School of Public Health and Statistics at Faculty of Arts and Sciences, Harvard University.
Harvard University · USColumbia University · USDuke University · USUniversity of Hong Kong · HKUniversity of Utah · US

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI SUSAN A KORRICK · 1985 to 2026
$44.6M
Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Rayjean J. Hung · 2017 to 2026
$23.7M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Spatial patterns of metals and metal mixtures in drinking waterP42ES030990 · NIEHS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI LU, QUAN · 2020 to 2024
$8.3M
Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.U01HG009088 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG, NEALE, BENJAMIN MICHAEL · 2016 to 2021
$5.1M
Leveraging Family Data to Identify Genetic Variants for Sleep ApneaR01HL113338 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI LIN, XIHONG, REDLINE, SUSAN S. · 2012 to 2016
$4.4M
Predictive Modeling of the Functional and Phenotypic Impacts of Genetic VariantsU01HG012064 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Manuel Garber, XIHONG LIN · 2021 to 2026
$4.0M
Joint Interdisciplinary Training Program in Biostatistics & Computational BiologyT32GM074897 · NIGMS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HUTTENHOWER, CURTIS, LIN, XIHONG · 2005 to 2019
$3.7M
NCI NIH HHS P01 CA134294NCI NIH HHS R35 CA197449NCI NIH HHS U19 CA203654NHGRI NIH HHS U01 HG009088NHGRI NIH HHS U01 HG012064NHLBI NIH HHS R01 HL113338NIEHS NIH HHS P30 ES000002NIEHS NIH HHS P42 ES030990NIGMS NIH HHS T32 GM074897
6 · The paper itself

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.

Indexed as

Causal mediation analysisComposite nullDivide-aggregate composite-null testGenome-wide epigenetic studiesHypothesis testingIndirect effectsMediation effectsProportions of true nulls

Identifiers

PMID35989709
PMCPMC9385159
OpenAlexW3152499279

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.