SynthesisGenome biology2019
Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies.
Synthesis in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
28 citing papers in PubMed, 5 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Epigenome-wide association study of circulating interleukin-6 connects DNA methylation to immunometabolic and inflammatory health.Communications biology · 2026Pooled it
- Pooled it
- A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes.Genome medicine · 2023Pooled it
- A systematic review of neuroimaging epigenetic research: calling for an increased focus on development.Molecular psychiatry · 2023Pooled it
- Prenatal medication exposure and epigenetic outcomes: a systematic literature review and recommendations for prenatal pharmacoepigenetic studies.Epigenetics · 2022Pooled it
- Epigenetic age acceleration in peripheral blood correlates with brain-MRI age acceleration.Brain : a journal of neurology · 2025Article
- Expression patterns of melanin-related genes are linked to crypsis and conspicuousness in a pumpkin toadlet.Molecular ecology · 2025Article
- Guidance for the design and analysis of cell-type-specific DNA methylation epidemiology studies.Briefings in bioinformatics · 2025Article
- On the analysis of metabolite quantitative trait loci: Impact of different data transformations and study designs.Science advances · 2025Article
- regionalpcs improve discovery of DNA methylation associations with complex traits.Nature communications · 2025Article
- Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.NPJ systems biology and applications · 2024Article
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- Placental microRNAs relate to early childhood growth trajectories.Pediatric research · 2023Article
- A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data.Clinical epigenetics · 2023Article
- Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies.Epigenetics · 2022Article
- Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.Genome biology · 2022Article
- Designing Studies for Epigenetic Biomarker Development in Autoimmune Rheumatic Diseases.Rheumatology and immunology research · 2022Review
- Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.BMC genomics · 2022Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.
resultsWe tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.
conclusionsResults were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.
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Registered trials
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.