Completed Mental Health Psychology & Behaviour

Exploiting genomic approaches to identify the environmental basis of depression

Funder Wellcome Charity-funded
Recipient organisation University of Edinburgh
Funding £2.5M
Period Jan 2021 — Dec 2025

In plain English

AI plain-English summary

Depression is now understood to be partly inherited, but the environmental triggers that turn genetic risk into illness remain largely unknown. This researcher will compare the DNA of people with and without depression, looking for patterns in how genes interact with lifestyle factors like smoking, drinking, and obesity. By measuring chemical changes to DNA—known as epigenetic marks—that accumulate in response to life experiences, the team aims to build a more accurate picture of which environmental exposures actually cause depression, rather than merely coinciding with it. If successful, the work could transform prevention. Instead of waiting for someone to become depressed, clinicians might one day use a blood test or questionnaire to identify individuals whose genetic and epigenetic profiles put them at high risk, then intervene early with targeted lifestyle advice or monitoring. The findings could also help disentangle cause from correlation in large-scale health datasets, improving how researchers study the interplay between nature and nurture in mental illness. This is fundamental science with a clear translational goal: refining prediction tools until they are reliable enough for real-world use in prevention programmes.

View original technical description
Despite major advances in our understanding of depression’s genetic architecture, there remain major gaps in our understanding of its environmental risk factors that impede prevention. Using studies of both related and unrelated individuals, I will identify the genetic changes and behaviours in those individuals that lead to depression in those individuals, as well as in their relatives. I will compare these changes with the genetic signatures of lifestyle factors (e.g. obesity) and behaviours, such as smoking and alcohol consumption, to identify if they are environmental risk factors for depression. In a complimentary approach, I will measure epigenetic changes in DNA methylation associated with depression and its risk factors. I will use optimise these epigenetic measures to improve the prediction of depression and the measurement of its risk factors. Finally, I will optimise the prediction of depression and its risk factors by applying these findings to prospective longitudinal dataset where depression and environmental risk factors have been measured on multiple occasions. These studies will be used to refine the accuracy of each predictor and estimate how it would behave in unseen samples of individuals, in whom it may be applied for prevention in future studies.

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Researchers

Andrew McIntosh (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Decoding Depression: Integrating Genetics, Environment, and Clinical Data to Predict Prognosis and Treatments
Using multiple data sources to stratify depression and identify more targeted drug treatments
Gene-by-environment interactions in depression
Examining the causes and consequences of sub-types of depression across the life course
Using genetics to understand the complex relationships between obesity and depression in diverse global settings

Original classification

Investigator Award in Science

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.