An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays

• Type of Material: Article
• Authors: Yuanchao Zheng, Boston University; Kathryn L. Lunetta, Boston University; Chunyu Liu, Boston University; Seyma Katrinli, Emory University; Alicia K Smith, Emory University; Mark W. Miller, Boston University; Mark W. Logue, Boston University
• Language: English
• Date: 2022-09-01
• Publisher: Taylor & Francis
• Publication Version: Final Published Version
• Copyright Statement: This work was authored as part of the Contributor’s official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 USC 105, no copyright protection is available for such works under US Law.
• Final Published Version (URL): https://doi.org/10.1080%2F15592294.2022.2115600
• Title of Journal or Parent Work: Epigenetics
• Volume: 17
• Issue: 13
• Start Page: 2241
• End Page: 2258
• Grant/Funding Information: This work was supported by the Biomedical Laboratory Research and Development, VA Office of Research and Development [I01BX003477]; National Institute of Mental Health [R21MH102834]; National Institute of Mental Health [1R01MH108826].; This work was funded by I01BX003477, a VA BLR&D grant to MW Logue, R21MH102834 to MW Miller, 1R01MH108826 to AK Smith/MW Logue/Nievergelt/Uddin, and the Translational Research Center for TBI and Stress Disorders (TRACTS), a VA Rehabilitation Research and Development (RR&D) Traumatic Brain Injury Center of Excellence (B9254-C) at VA Boston Healthcare System.
• Keywords: differentially methylated regions; DNA methylation; genome-wide false positive rate
• Research Categories: Biology, Microbiology; Biology, General

Abstract

Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple loci that are associated with a phenotype. We examined the genome-wide false positive (GFP) rates of five widely used DMR methods: comb-p, Bumphunter, DMRcate, mCSEA and coMethDMR using both Illumina HumanMethylation450 (450 K) and MethylationEPIC (EPIC) data and simulated continuous and dichotomous null phenotypes (i.e., generated independently of methylation data). coMethDMR provided well-controlled GFP rates (~5%) except when analysing skewed continuous phenotypes. DMRcate generally had well-controlled GFP rates when applied to 450 K data except for the skewed continuous phenotype and EPIC data only for the normally distributed continuous phenotype. GFP rates for mCSEA were at least 0.096 and comb-p yielded GFP rates above 0.34. Bumphunter had high GFP rates of at least 0.35 across conditions, reaching as high as 0.95. Analysis of the performance of these methods in specific regions of the genome found that regions with higher correlation across loci had higher regional false positive rates on average across methods. Based on the false positive rates, coMethDMR is the most recommended analysis method, and DMRcate had acceptable performance when analysing 450 K data. However, as both could display higher levels of FPs for skewed continuous distributions, a normalizing transformation of skewed continuous phenotypes is suggested. This study highlights the importance of genome-wide simulations when evaluating the performance of DMR-analysis methods.