Accurate inference of DNA methylation data: statistical challenges lead to biological insights

DNA methylation is an epigenetic modification widely believed to act as a repressive signal of gene expression. Whether or not this signal is causal, however, is currently under debate. Recently, a groundbreaking experiment probed the influence of genome-wide promoter DNA methylation on transcription and concluded that it is generally insufficient to induce repression. However, the previous study did not make full use of statistical inference in identifying differentially methylated promoters. In this talk, I’ll detail the pressing statistical challenges in the area of DNA methylation sequencing analysis, as well as introduce a statistical method that overcomes these challenges to perform accurate inference. Using both Monte Carlo simulation and complementary experimental data, I’ll demonstrate that the inferential approach has improved sensitivity to detect regions enriched for downstream changes in gene expression while accurately controlling the False Discovery Rate. I will also highlight the utility of the method through a reanalysis of the landmark study of the causal role of DNA methylation. In contrast to the previous study, our results show that DNA methylation of thousands of promoters overwhelmingly represses gene expression.