An overview of DNA methylation-derived trait score methods and applications

Abstract

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites (“probes”) and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study

“…DNAm, the addition of a methyl group at a cytosine-phosphate-guanine (CpG) site, regulates gene expression and impacts cellular function 19,20 . In 2022, Barbu et al 21 performed a methylome-wide association study (MWAS) of self-reported antidepressant exposure in a subset of participants in Generation Scotland (GS, N = 6,428) and the Netherlands Twin Register (NTR, N = 2,449) 21 , identifying altered DNAm near to genes involved in the innate immune response in those exposed to antidepressants 21 .…”

A methylome-wide association study of major depression with out-of-sample case-control classification and trans-ancestry comparison

“…DNAm is dynamic and is associated with changes in environmental and lifestyle factors, including smoking 6 , alcohol 7 , and BMI 8 , all factors that are implicated in MD 3 . A recent study 9 identified associations between methylation scores (MS), calculated using methylome-wide association study (MWAS) summary statistics for several relevant lifestyle factors and MD 10 . These environmental MS measures were able to capture additional variation associated with MD when added to direct lifestyle measures, and it is thought this may be due to their ability to act as an archive of environmental exposure.…”

Measuring technical variability in Illumina DNA methylation microarrays

“…Improvements to analytical pipelines for array-based measurement of DNAm remain a high priority within the field, as technical limitations to data quality and consistency across batches currently interfere with the reliable detection of differences in DNAm, limiting the feasibility of DNAm microarray technologies for both research and clinical applications 3,5 . There is a general agreement regarding the need to account for batch and positional effects and other technical artifacts which influence microarray data and might otherwise result in spurious relationships 3 . To this end, many preprocessing methods have been developed which reduce the impact of positional effects and improve reliability.…”

“…In recent years, DNA methylation (DNAm) has received considerable interest as a potential biomarker for numerous clinically relevant traits, including disease and health risks 1,2 . In oncology, DNAm microarray-based assays which predict tumor site of origin have begun to see approved clinical use in the US and European Union for diagnostic purposes and for classifying cancers of unknown primary origin 3,4 , and numerous methylation-derived risk scores (MRS) and predictive models for traits such as smoking status, body weight, and age are well-established in the scientific literature 3 .…”