ArticleBiometrics2021
A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Brain-targeted epigenetic effects of two emerging psychoplastogens: ketamine & MDMA.Translational psychiatry · 2026Article
- A Functional Approach to Testing Overall Effect of Interaction Between DNA Methylation and SNPs.Statistics in medicine · 2026Article
- A novel high-dimensional model for identifying regional DNA methylation QTLs.Biostatistics (Oxford, England) · 2025Article
- Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data.Clinical epigenetics · 2023Article
- Article
- A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.Biometrics · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.
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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 Socratic graph.