ArticleBiostatistics (Oxford, England)2019
Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.
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Who cites it
107 citing papers in PubMed.
- DiffMethylTools: a toolbox for the detection, annotation, and visualization of differential methylation.Life science alliance · 2026Article
- Epigenetic and metabolic reprogramming of innate immune cells establishes immunological memory in the Schistosomiasis vector snail Biomphalaria glabrata.PLoS pathogens · 2026Article
- RegRegSEA: a web server for regulatory region set enrichment analysis of epigenomic data.Nucleic acids research · 2026Article
- Convergence and divergence of DNA methylation and gene expression patterns in neopolyploid Arabidopsis kamchatica.Nature communications · 2026Article
- Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Whole genome methylation profiling of menstrual stem cells identifies novel biomarkers for endometriosis.Communications medicine · 2026Article
- Convergence of aging- and rejuvenation-related epigenetic alterations on PRC2 targets.Molecular systems biology · 2026Article
- Causal evidence linking injury-associated DNA methylation to the risk of developing depression or post-traumatic stress disorder.Medicine · 2026Article
- MidbrainbioRxiv : the preprint server for biology · 2026Article
- Cell-specific DNA methylation in human alpha and beta cells regulates gene expression in type 2 diabetes.Nature metabolism · 2026Article
- Persistent transcriptomic changes following repeated exposure to wood smoke in nonhuman primate airway epithelial cells.Environmental epigenetics · 2026Article
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- A novel high-dimensional model for identifying regional DNA methylation QTLs.Biostatistics (Oxford, England) · 2025Article
- Persistent transcriptomic changes following repeated exposure to wood smoke in non-human primate airway epithelial cells.bioRxiv : the preprint server for biology · 2025Article
- Developmental programming: Differing impact of prenatal testosterone and prenatal bisphenol-A -treatment on hepatic methylome in female sheep.Molecular and cellular endocrinology · 2025Article
- Pan-Cancer Detection Through DNA Methylation Profiling Using Enzymatic Conversion Library Preparation with Targeted Sequencing.International journal of molecular sciences · 2025Article
- SMCHD1 maintains heterochromatin, genome compartments and epigenome landscape in human myoblasts.Nature communications · 2025Article
- Accumulation of Epigenetic Noise in the Aging Corneal Epithelium and Its Possible Mechanism.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- Improved tumor-type informed compared to tumor-informed mutation tracking for ctDNA detection and microscopic residual disease assessment in epithelial ovarian cancer.Journal of experimental & clinical cancer research : CR · 2025Article
47 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate.
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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.