1. Detecting differentially methylated regions with multiple distinct associations
- Author
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Josée Dupuis, Samantha Lent, Sheryl L. Rifas-Shiman, Patrice Perron, Marie-France Hivert, Ching-Ti Liu, Luigi Bouchard, and Andres Cardenas
- Subjects
Male ,Cancer Research ,Adolescent ,Computational biology ,Biology ,Epigenesis, Genetic ,03 medical and health sciences ,0302 clinical medicine ,Text mining ,Genetics ,Humans ,Computer Simulation ,Prospective Studies ,030304 developmental biology ,0303 health sciences ,Models, Genetic ,business.industry ,Gene Expression Profiling ,Infant ,DNA Methylation ,Fetal Blood ,Differentially methylated regions ,Gene Expression Regulation ,CpG site ,030220 oncology & carcinogenesis ,Cohort ,CpG Islands ,Female ,business ,Research Article ,Type I and type II errors - Abstract
Aim: We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff. Materials & methods: We used a simulation study and real data analysis to evaluate performance. Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood. Results: Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1–2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods. Conclusion: This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.
- Published
- 2021
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