Statistical methods for detecting differentially methylated loci and regions

Abstract

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Small-Magnitude Effect Sizes in Epigenetic End Points are Important in Children’s Environmental Health Studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group

“…Thus, maximizing data reliability via stringent quality control and data processing procedures, as well as statistical power to detect small-scale changes, is crucial for identifying environmental epigenetic links. Here we discuss these principles with regard to birth cohort and other longitudinal children’s studies evaluating environmental factors as they apply to two widely used bisulfite-treatment methodologies: a ) quantitative targeted DNA methylation analysis by PSQ and b ) epigenome-wide analysis with the Infinium 450K or EPIC array [we refer readers to recent publications that provide more detail on specific aspects of the 450K array pipeline, data processing, and analysis (Heiss and Brenner 2015; Maksimovic et al 2015; Morris and Beck 2015; Robinson et al 2014; Yuan et al 2015)].…”

“…This includes additional validation of the functional impact of identified DMRs in terms of gene expression (Robinson et al 2014; Yuan et al 2015). Further, sensitivity analysis on DMR calls has been rare to date.…”

Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data

“…Robinson et al provides an informative review of existing methods [88]. A comprehensive and objective comparison of the methods is still lacking, partly because it is difficult to obtain gold standards.…”

Crop Epigenomics: Identifying, Unlocking, and Harnessing Cryptic Variation in Crop Genomes

“…Currently, the majority of DMR finding methods starts from identifying a DMS (differentially methylated site) and then using either physical or statistical methods such as a HMM (Hidden Markov Model) to combine multiple DMSs into a single DMR (Robinson et al, 2014; Seymour et al, 2014). Certainly many efforts have been made to advance this field and a variety of different statistics-based programs have been released (Robinson et al, 2014), however this diversity also hampers the comparative analysis of different studies and in some cases, increases the difficulty to link DMRs with gene expression profiles and phenotypes. Other challenges in discovering DMRs include low coverage data and how multiple samples with replicates are handled.…”