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How do genes and the environment influence depression?
Safe People
Organisation name
University of Edinburgh
Organisation sector
Academic Institute
Applicant name(s)
Alex Kwong
Funders/ Sponsors
Safe Projects
Project ID
OFHS260011
Lay summary
Depression is one of the most severe disorders around the world, thought to affect over 300 million people. Despite best efforts to treat and better understand the disorder, our understanding of depression is far from complete. Recent research has shown that depression is partly genetic and we can use genes to better understand the biological basis of depression. Studies like Our Future Health have important data on depression, genetics and other lifestyle that can be used to improve our understanding and lead to better future treatments for depression. Our aim is to identify how genes and the environment can lead to depression. We also aim to examine how social, psychological and behavioural factors, such as socio-economic status, smoking and drinking, and physical health could act as risk factors for depression and vice versa. Our research questions include: 1) How are genes associated with different depression outcomes? 2) How do social, psychological and behavioural factors contribute to the risk of depression and vice versa? 3) How do genetic and environmental factors interact with one another to influence the risk of depression, and vice versa? 4) How can we use genetic data to better understand whether environmental factors cause depression? Depression is a leading cause of disability worldwide, predicted to be the leading cause by 2030. Most risk for depression is influenced by environmental factors, however, depression has a significant genetic component. Understanding of genetic factors has advanced recently, but how these combine with environmental factors to influence depression remains unclear. Previous studies have identified social, psychological and behavioural factors, such as, socio-economic status, substance use, and physical health as key environmental risk factors, however, it is difficult to determine from observational studies whether these factors directly cause, or are simply correlated with, depression. Furthermore, since depression may be a cause of some of these factors, it's unclear how they interact with each other and change over time. Due to advances in statistics, we can now use datasets like Our Future Health to begin to address these research questions given their substantial size and rich available data. By using genetic information, we can first identify genetic variants that are associated depression, thereby improving our biological understanding of the disease. We can then use genetics to identify risk factors for depression that can be changed to reduce risk, using advanced statistics which are less susceptible to biases than purely observational studies.
Public benefit statement
Depression is common and for many individuals it can lead to poor quality of life, poor health and even reduced lifespan. Despite this, many people do not receive the right treatment or support, and current approaches do not work equally well for everyone. There is a clear unmet need to better understand why some people develop depression while others do not. This study aims to use Our Future Health to improve our understanding of depression by examining both genetic and environmental factors. The added value and benefit for the public will be a better understanding of how lifestyle, environmental and genetic factors influence the risk of depression, and how depression can affect other behaviours and traits. This research can help identify people who are at higher risk earlier in life. It can also clarify which risk factors may directly cause depression and which are potentially preventable. In the long term, this work could support earlier treatment, more targeted support strategies, and more personalised approaches to mental health care, helping to reduce the personal and societal burden of depression.
Request category type
Public Health Research
Other approval committees
Project start date
20/08/2026
Latest approval date
24/04/2026
Safe Data
Dataset(s) name
Safe Setting
Access type
TRE
Safe Outputs
Link to research outputs