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Identifying disease-associated changes in DNA methylation can help to gain abetter understanding of disease etiology. Bisulfite sequencing technologyallows the generation of methylation profiles at single base of DNA. Wepreviously developed a method for estimating smooth covariate effects andidentifying differentially methylated regions (DMRs) from bisulfite sequencingdata, which copes with experimental errors and variable read depths; thismethod utilizes the binomial distribution to characterize the variability inthe methylated counts. However, bisulfite sequencing data frequently includelow-count integers and can exhibit over or under dispersion relative to thebinomial distribution. We present a substantial improvement to our previouswork by proposing a quasi-likelihood-based regional testing approach whichaccounts for multiplicative and additive sources of dispersion. We demonstratethe theoretical properties of the resulting tests, as well as their marginaland conditional interpretations. Simulations show that the proposed methodprovides correct inference for smooth covariate effects and captures the majormethylation patterns with excellent power.

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