Detecting differentially methylated regions in bisulfite sequencing data using quasi-binomial mixed models with smooth covariate effect estimates

TL;DR

This paper introduces an advanced statistical method for detecting differentially methylated regions in bisulfite sequencing data, effectively handling overdispersion and low counts to improve inference accuracy.

Contribution

It develops a quasi-likelihood-based regional testing approach that accounts for dispersion, enhancing the detection of methylation differences over previous methods.

Findings

Method accurately detects methylation patterns with high power

Provides correct inference for smooth covariate effects

Handles overdispersion and low-count data effectively

Abstract

Identifying disease-associated changes in DNA methylation can help to gain a better understanding of disease etiology. Bisulfite sequencing technology allows the generation of methylation profiles at single base of DNA. We previously developed a method for estimating smooth covariate effects and identifying differentially methylated regions (DMRs) from bisulfite sequencing data, which copes with experimental errors and variable read depths; this method utilizes the binomial distribution to characterize the variability in the methylated counts. However, bisulfite sequencing data frequently include low-count integers and can exhibit over or under dispersion relative to the binomial distribution. We present a substantial improvement to our previous work by proposing a quasi-likelihood-based regional testing approach which accounts for multiplicative and additive sources of dispersion. We demonstrate the theoretical properties of the resulting tests, as well as their marginal and conditional interpretations. Simulations show that the proposed method provides correct inference for smooth covariate effects and captures the major methylation patterns with excellent power.