Key Takeaways
- Heritability: Depression has an estimated heritable component of 30-40%, though it is influenced by many small genetic variants.
- Current Limitations: Known genetic variants currently account for only 2% of depression risk, with 98% attributed to environmental factors and unknown variants.
- Treatment Challenges: Only one-third of patients respond to their first antidepressant, highlighting the need for personalized medicine.
- Research Progress: Scientists have identified 102 independent genetic variants associated with depression to develop polygenic risk scores.
- Future Outlook: Large-scale studies like GLAD are bridging the gap between genomic research and clinical application for better screening and therapies.
Understanding the genetic causes of depression
Ongoing research is focused on fully understanding the causes of depression. Through twin studies, researchers have identified that depression has a heritable component estimated to be between 40–50%, and potentially higher for severe forms. Importantly, this does not mean that every case of depression is half genetic. It means that at a population level, genetic variation accounts for roughly half of the variability in risk. In some individuals, genetic factors may play a larger role; in others, the contribution may be minimal. The remaining risk is shaped by environmental and psychological factors, many of which are still being characterized. However, as depression is a heterogeneous condition, it is influenced by many different genetic variants and each individual genetic variant has a very small effect on risk. As a result, researchers need to study very large numbers of individuals to discover the numerous variants that play a role in depression. To overcome this challenge and recruit the large number of participants required to conduct the study, researchers around the world are pooling their resources together, and collaborating with genomic research platforms to onboard patients much faster through their global communities with genomic information.
The Psychiatric Genomics Consortium reported new findings in a paper titled “Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of prefrontal brain regions.” This case-control study compared the genetic information of 246,363 people diagnosed with depression with a control group of 561,190 healthy individuals, to find gene variants that occur more frequently in those with depression. Researchers identified 102 different genetic variants associated with depression. Critically, no single gene causes depression on its own. Each variant contributes a small increase in predisposition, and depression is not inherited in the way that single-gene disorders such as cystic fibrosis are. These genetic variants were aggregated to derive the cumulative risk for depression, known as a polygenic risk score. Researchers used the polygenic risk score “to assess the predictive ability of the current genome-wide meta-analysis of depression.”
While these findings are very promising, this is just the beginning of understanding the genetic factors in depression. Researchers emphasise that the different genetic variants only account for 2% of the risk for developing depression. The remaining 98% is influenced by environmental factors and unknown variants which require further research. “That 98% of unknown information is always going to swamp that 2% of known information, and until that balance changes, there is no utility in a genetic test for depression,” says Dr Cathryn Lewis, Professor of Genetic Epidemiology and Statistics at King's College London. Dr Lewis discusses these findings and the current limits of clinical application on The Genetics Podcast.
Yet even as individual variants explain a small share of overall risk, family history patterns clearly indicate a meaningful genetic component. Individuals with a parent or sibling who has experienced major depression face a notably higher risk than the general population, and this risk increases further when the affected relative has experienced recurrent depression with early onset. The gap between what family studies reveal and what genome-wide studies have identified so far points to the scale of research still required to translate genetic findings into clinical tools.
We are not yet at the stage to use polygenic risk scores or offer genetic tests at an individual level to predict the risk of depression. One reason is that genetic predisposition does not act in isolation. Research consistently shows that environmental factors, including severe childhood trauma, emotional neglect, and significant life stress, interact with genetic vulnerability to influence whether depression develops. The Paris Brain Institute notes that this predisposition is often associated with an environmental trigger, such as bereavement, relationship breakdown, or job loss. These gene-environment interactions are difficult to capture through genetic data alone, which is why current polygenic risk scores remain limited in clinical utility.
However, these large cumulative studies capture information that, with continued research, will clarify the causal pathways of depression. These insights will move us closer to applying research in a clinical setting for better screening, preventive therapies, and precision medicine treatments.
Understanding patient response to treatment
Using genetic variants to guide treatment selection for depression is another area of active research. Currently, only one-third of patients with depression respond to the first antidepressant they try, and it can take up to 8 weeks to determine whether the antidepressant is effective. This means that many patients cycle through multiple medications before finding one that works, prolonging suffering and increasing the burden on patients and healthcare systems. Genetically informed treatment selection offers a potential path to matching individuals with more suitable therapies earlier in the process.
One study titled Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder by Héléna Gaspar, King's College London, investigated drug-target relationships that could be impacting major depressive disorder (MDD).
The study examined causal genetic variants of MDD located around key drug-target hubs, including:
- Calcium channels
- Dopamine
- Serotonin
- Histamine
- GABA receptors
- Estrogen
Identifying these hubs helps ensure patients receive the most appropriate drugs to inhibit or enhance specific effects.
This knowledge will help to ensure that patients receive the most appropriate drug to inhibit or enhance the effect of the target. However, the study highlights the challenge of identifying the direction of effect when using data from genome wide association studies, recommending that further research be undertaken before drawing conclusions. The published report states “the relationship between a drug and a phenotype cannot easily be inferred; an association may reflect either a depression-inducing effect or an antidepressant effect. We partially address this issue via imputation and prediction of gene expression, but pharmacological, molecular, and clinical validation will be needed before drawing definitive conclusions. However, we suggest that our findings may represent a source of new therapeutic hypotheses for MDD—a common and currently only partially treatable disorder.”
In the UK, research is being undertaken by the Genetic Links to Anxiety and Depression Study (GLAD). Leader of the GLAD Study and the NIHR Mental Health BioResource, Dr Gerome Breen, states that "by recruiting 40,000 volunteers willing to be re-contacted for research, the GLAD Study will take us further than ever before. It will allow researchers to solve the big unanswered questions, address how genes and environment act together and help develop new treatment options."
The GLAD study's emphasis on building a recontactable cohort reflects a structural requirement of depression research. Depression is often characterized by multiple major depressive episodes spaced over time, which means that single-point data collection is insufficient to capture the full trajectory of the condition. Longitudinal engagement with participants, including the ability to recontact them for follow-up studies, is essential for understanding how genetic and environmental factors interact across episodes. As genomics in mental health continues to advance, the ability to maintain these research relationships over time will be as important as the initial genetic findings themselves.