Abstract
DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome while taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method's efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600bp region in the promoter of the Ifitm3 gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis), and a consistent change in Ifitm3 expression. Our ABBA analysis allowed us to propose a new role for Ifitm3 in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated with Ifitm3 repression in the rat strain susceptible to glomerulonephritis.
Highlights
We employ a fully Bayesian approach, which models the random sampling process of the whole-genome bisulfite sequencing (WGBS) experiment, and where all the unknown quantities are specified by probability distributions
Several intrinsic features of WGBS data are incorporated into approximate Bayesian bisulfite sequencing analysis (ABBA): for instance, the variability in DNA methylation between the replicates within each group is modeled through a random effect with a specific within-group variance (Figure 1A)
Beyond statistical power considerations related to sample size (Rakyan et al 2011) or interpretability of epigenome-wide association studies (Birney et al 2016), our ability to identify accurately changes in DNA methylation localized to specific genomic loci is influenced by multiple factors inherently correlated to data quality
Summary
To demonstrate the benefits of adopting ABBA over existing approaches, we report a comprehensive simulation study, where we benchmarked ABBA against five commonly used alternative methods (Fisher Exact Test, BSmooth, MethylKit, MethylSig, and DSS), considered a proposed new one (metilene), and assessed the effect of a different biological and experimental conditions (by varying parameters related to data integrity and quality of the signal) on the performance of each method The results from this benchmark clearly indicate that ABBA is the best performing method, being both robust to changes in factors affecting data quality (e.g., sequencing coverage and errors associated with the methylation call), and level of noise in methylation signal. We integrated the DMR results of ABBA with transcription factor binding site analysis, RNA-seq and ChIP-seq data generated in the same system, and, in this, we revealed a previously unappreciated role for the Ifitm gene in the pathogenesis of glomerulonephritis, providing a proof-of-concept for real data applications of the ABBA approach
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