Methodological challenges in constructing DNA methylation risk scores

ABSTRACT

Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.

KEYWORDS:

• Polygenic epidemiology
• polygenic risk scores
• weighting strategies
• genetic risk scores
• prediction models
• epigenetic risk score

Acknowledgments

We thank Elisabeth B. Binder, Michael J. Meaney and Michael S. Kobor for fruitful discussions which brought this collaboration forward.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

AH received a research fellowship from the Deutsche Forschungsgemeinschaft (DFG; HU 2731/1-1).