Modelling complex phenotypes using DNA methylation (DNAm) is becoming increasingly common in Epigenetic Epidemiology. This process often includes the use of weighted DNAm “scores” to differentiate between classes of categorical exposures, estimate continuous exposures and/or predict disease outcomes. The reason DNAm can do this is because it bridges the gap between your biology and your environment; one function of DNAm is that it will work to alter gene expression in response to an environmental stimulus. Accordingly, it can be thought of as a “biosocial archive”.

A recent study found that a DNAm proxy of smoking explained more variance in certain mental and physical health outcomes than someone self-reporting their smoking status. This is important because it highlights that DNAm can either augment or improve on how we define self-report traits such as smoking, and could potentially extend to other traits such as alcohol consumption, BMI and educational attainment. Further, using DNAm either in conjunction with, or instead of self-report, may allow researchers to identify targeted interventions for a variety of disease outcomes.

Accordingly, in this project we will be investigating whether DNAm proxies vs self-report smoking, BMI, alcohol consumption and educational attainment, respectively, explain more variance in cardiovascular, mental health, socioeconomic, clinical, metabolic and addiction outcomes in ALSPAC.