Depression is a highly prevalent disorder and we still have a lot to learn about its biological underpinnings. Our study identifies hundreds of additional genetic variants that play a role in depression. These findings show depression is highly polygenic and open up downstream pathways to translate these findings into better care for people with depression.
Professor Cathryn Lewis, Professor of Genetic Epidemiology & Statistics at King's IoPPN and study co-lead
14 January 2025
Global study pinpoints genes for depression across ethnicities
Researchers at King's College London and University of Edinburgh have identified new genetic risk factors for depression across all major global populations for the first time, allowing scientists to predict risk of depression regardless of ethnicity.
The world’s largest and most diverse genetic study ever into major depression has revealed nearly 300 previously unknown genetic links to the condition.
Published in Cell, the study found that 100 of the newly discovered genetic variations – small differences in the DNA sequence that makes up a gene – were identified due to the inclusion of people of African, East Asian, Hispanic and South Asian descent.
Previous research into the genetics of depression has focused primarily on white populations that originally descended from people living in Europe. Therapies developed using genetic approaches may, therefore, not be effective in other ethnicities, widening existing health inequalities.
Each single genetic variant has a very small effect on the overall risk of developing depression. If a person has multiple variants, these small effects can add up, increasing their risk.
The international research team, led by the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) at King’s College London and the University of Edinburgh, were able to more accurately predict an individual’s risk of depression by taking into account the newly identified variants.
The team looked at anonymised genetic data from more than five million people in 29 countries worldwide. One in four individuals included in the study were from non-European ancestries.
Researchers identified a total of 700 variations in the genetic code of individuals linked to the development of depression, almost half of which had never been associated with the condition before, implicating 308 specific genes.
The identified genetic variants were linked to neurons – a type of brain cell – across multiple brain regions, including areas which control emotion.
The findings offer new insight into depression’s impact on the brain and present possible new targets for treatment, experts say.
The researchers highlighted existing drugs pregabalin and modafinil – used to treat chronic pain and the sleeping condition narcolepsy, respectively – which could potentially be repurposed for the treatment of depression, based on the study findings. However, they caution that further studies and clinical trials are needed to explore the potential of the drugs in patients with depression.
There are huge gaps in our understanding of clinical depression that limit opportunities to improve outcomes for those affected. Larger and more globally representative studies are vital to provide the insights needed to develop new and better therapies, and prevent illness in those at higher risk of developing the condition.
Professor Andrew McIntosh, Professor of Biological Psychiatry at the University of Edinburgh’s Centre for Clinical Brain Sciences and study co-lead
The study was funded by NIH, Wellcome and the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre.
The research team from the Psychiatric Genomics Consortium involved scientists from all continents, including studies from South Africa, Brazil, Mexico, the USA, Australia, Taiwan and China.
"Genome-wide study of half a million individuals with major depression identifies 697 independent associations, infers causal neuronal subtypes and biological targets for novel pharmacotherapies" was published in Cell. DOI: 10.1016/j.cell.2024.12.002
In this story
Professor of Genetic Epidemiology & Statistics