The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation Analysis

Elior Rahmani; Brandon Jew; Eran Halperin

doi:10.3389/fbinf.2021.792605

The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation Analysis

Front Bioinform. 2022 Jan 18:1:792605. doi: 10.3389/fbinf.2021.792605. eCollection 2021.

Authors

Elior Rahmani¹, Brandon Jew², Eran Halperin^{3

4

5

6}

Affiliations

¹ Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States.
² Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, United States.
³ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, United States.
⁴ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
⁵ Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States.
⁶ Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, United States.

Abstract

Calling differential methylation at a cell-type level from tissue-level bulk data is a fundamental challenge in genomics that has recently received more attention. These studies most often aim at identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention has been given to the fact that this directionality assumption may not hold and can consequently affect statistical power and control for false positives. We demonstrate that misspecification of the model directionality can lead to a drastic decrease in performance and increase in risk of spurious findings in cell-type-specific differential methylation analysis, and we discuss the need to carefully consider model directionality before choosing a statistical method for analysis.

Keywords: DNA methyaltion; EWAS; cell-type-specific; computational biology; differential methylation; epigenome-wide association studies; statistical analysis.