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 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 introduce a statistical method for the detection and accurate inference of differential methylation and present the results of a reanalysis of the previous study using the new approach. 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. Results from the reanalysis show that DNA methylation of thousands of promoters overwhelmingly represses gene expression. Light refreshments for seminar guests will be served at 3:10 p.m. in 1655
Department of BiotsatisticsAccurate inference of DNA methylation data: statistical challenges lead to biological insights
Keegan Korthauer, Ph.D., Postdoctoral Research Fellow in Biostatistics and Computational Biology - Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
January 8, 2019
3:30 pm - 5:00 pm
1655 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029
Sponsored by: Department of Biotsatistics
Contact Information: Zhenke Wu (zhenkewu@umich.edu) & Peisong Han (peisong@umich.edu)
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 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 introduce a statistical method for the detection and accurate inference of differential methylation and present the results of a reanalysis of the previous study using the new approach. 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. Results from the reanalysis show that DNA methylation of thousands of promoters overwhelmingly represses gene expression. Light refreshments for seminar guests will be served at 3:10 p.m. in 1655
