Controlling Bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution
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Abstract
We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.
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We used a Bayesian method to control p-value inflation using the R package bacon before combining estimated effect sizes and standard errors across cohorts with a random effects meta-analysis.
“We used a Bayesian method to control p-value inflation using the R package bacon (van Iterson et al., 2017) before combining the estimated effect sizes and standard errors across cohorts with a random effects meta-analysis”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as van Iterson et al., 2017
The bacon R package uses a Bayesian method to adjust for inflation in EWAS p-values.
“The EWAS results from each cohort were processed using the bacon R package (van Iterson et al., 2017), which uses a Bayesian method to adjust for inflation in EWAS p-values.”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as van Iterson et al., 2017
Bacon correction was applied to obtain corrected effect sizes, standard errors, and P-values for each cohort.
“genomic correction using the bacon method [45], as implemented in the bacon R package, was applied to obtain bacon-corrected effect sizes, standard errors, and P-values for each cohort.”
From Distinct sex-specific DNA methylation differences in Alzheimer’s disease (2022) · cites this paper as [45]
In a typical epigenome-wide association study, small effects from many CpGs might be associated with the phenotype, so the genomic inflation factor would overestimate actual test-statistic inflation.
“Because in a typical epigenome-wide association study (EWAS), it is expected that small effects from many CpGs might be associated with the phenotype, the genomic inflation factor would overestimate actual test-statistic inflation”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [32]
The bacon method estimates inflation in EWAS based on empirical null distributions.
“Iterson et al. [32] developed a Bayesian method that estimates inflation in EWAS based on empirical null distributions, which is implemented in the Bioconductor package bacon”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [32]
The conventional genomic inflation factor is dependent on the expected number of true associations.
“the conventional genomic inflation factor (lambda or λ used interchangeably below) is dependent on the expected number of true associations”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [32]
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- Authors
- the BIOS Consortium, van Iterson, M., van Zwet, E. W., & Heijmans, B. T.
- Venue
- Genome biology
- Published
- 2017-01-27
- Volume
- 18
- Issue
- 1
- Pages
- 19-19
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC5273857
- OpenAlex
- W2580897959
- Cited by
- 421
- References
- 66
- Citations by year
- 2026: 55 · 2025: 57 · 2024: 59 · 2023: 56 · 2022: 44 · 2021: 52 · 2020: 43 · 2019: 32 · 2018: 13 · 2017: 9 · 2016: 1
- FWCI
- 18.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- VU Research Portal (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- NWO (184.021.007)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- epigenome-wide association study, transcriptome-wide association study, empirical null distribution, bias correction, Bayesian analysis, false positive rate control, statistical power, confounding adjustment, age, smoking, meta-analysis
- MeSH terms
- Age Factors, Bayes Theorem, Humans, Smoking, Meta-Analysis as Topic, Bias, Epigenesis, Genetic, Genome-Wide Association Study, Epigenomics, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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