  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.
