Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing

Keegan Korthauer, Sutirtha Chakraborty, Yuval Benjamini, Rafael A. Irizarry*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference.A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation.We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate.

Original languageAmerican English
Article numberkxy007
Pages (from-to)367-383
Number of pages17
JournalBiostatistics
Volume20
Issue number3
DOIs
StatePublished - 1 Jul 2019

Bibliographical note

Publisher Copyright:
© The Author(s) 2018.

Keywords

  • Bisulfite sequencing
  • Differential methylation
  • False discovery rate
  • Generalized least squares

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