B3808 - The association between epigenetic prediction signatures of complex traits and health outcomes in ALSPAC - 22/06/2021

B number: 
Principal applicant name: 
Ryan Langdon | University of Bristol (United Kingdom)
Chloë Fabbricatore, Matt Suderman
Title of project: 
The association between epigenetic prediction signatures of complex traits and health outcomes in ALSPAC
Proposal summary: 

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.

Impact of research: 
Generating evidence to support downstream use (or not) of DNA methylation to improve how epidemiologically-relevant phenotypes are defined
Date proposal received: 
Monday, 21 June, 2021
Date proposal approved: 
Tuesday, 22 June, 2021
Epidemiology, Addiction - e.g. alcohol, illicit drugs, smoking, gambling, etc., Behaviour - e.g. antisocial behaviour, risk behaviour, etc., Diabetes, Hypertension, Mental health, Obesity, Respiratory - asthma, Computer simulations/modelling/algorithms, Metabolomics, Microarrays, Statistical methods, Biological samples -e.g. blood, cell lines, saliva, etc., Biomarkers - e.g. cotinine, fatty acids, haemoglobin, etc., Physical - activity, fitness, function, Statistical methods, Blood pressure, BMI, Cardiovascular, Environment - enviromental exposure, pollution, Epigenetics, Metabolic - metabolism, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Microbiome