By Morgan Nwanguma
Millions of people worldwide struggle with depression and anxiety, often facing long and discouraging waits before finding an effective treatment. Now, scientists in Sweden, Denmark, and Germany are developing a genetic test that could change that, by predicting which medications are most likely to work for each individual.
Using a tool called polygenic risk scoring (PRS), researchers can analyze subtle variations in a person’s DNA that influence both mental health risk and response to medications. This approach could bring personalized treatment for depression and anxiety a step closer to reality.
The challenge of finding the right medication
Depression and anxiety are the most common mental health disorders globally — affecting an estimated 300 million and 301 million people, respectively, or about 8% of the world’s population. Yet for many, treatment remains a frustrating process of trial and error. Nearly half of all patients experience little or no benefit from their first prescription, often spending weeks or months testing multiple drugs before finding relief.
A novel genetic method of treatment
A collaborative team of researchers from Germany, Sweden, and Denmark believes they may have found a way to improve this process. Their innovative method uses polygenic risk scores to predict how an individual’s unique genetic makeup might affect their response to antidepressants or anti-anxiety medications.
With a single genetic test, clinicians could soon estimate which drugs are most likely to be effective for each patient – potentially reducing delays, side effects, and treatment failures.
Although this technique has so far been tested only on genetic databases rather than in real clinical settings, the results are highly encouraging. Lead author Professor Fredrik Åhs, from the Department of Psychology and Social Work at Mid Sweden University, hopes to move the research into clinical trials in the near future.
“We believe this technology could be used to develop more targeted tests. The long-term goal is a test that doctors can use to choose the right medicine, and looking at our genes is one way of doing it,” Åhs says. “We’re interested in looking into biomarkers as well. Hopefully, in the future, we’ll have a cheap and effective test that enables us to alleviate people’s suffering much faster.”
Building on genetic research from Aarhus University
The project originated two years ago when Professor Fredrik Åhs contacted Professor Doug Speed of the Center for Quantitative Genetics and Genomics at Aarhus University in Denmark. Åhs hoped to apply Speed’s advanced polygenic risk score (PRS) models to his ongoing research on mental health treatment.
Professor Speed has spent years developing sophisticated methods for analyzing complex human genetic data, with a particular focus on understanding how genes shape psychological traits and disorders.
“The last 10 years, we’ve been working towards using polygenic risk scores to predict disease. It’s very challenging because many diseases are caused by thousands of variations across the genome,” Speed explains. “It turns out that these polygenic risk scores can predict our response to drugs, which is a bit surprising, but a significant step forward.”
He has already developed polygenic risk score (PRS) models for several major psychiatric conditions including schizophrenia, anxiety, bipolar disorder, and depression – all of which were incorporated into this latest study.
What are polygenic risk scores?
Since the mapping of the human genome in the early 2000s, scientists have identified thousands of small DNA variations that can influence health and disease. Humans possess about 20,000 genes, each with multiple versions known as alleles. Some alleles are associated with a higher likelihood of developing certain disorders.
Researchers such as Professor Speed combine this genetic information to create polygenic risk scores, which estimate a person’s genetic predisposition to specific conditions by adding up the effects of many tiny DNA variations.
For example, when building a PRS for depression, scientists examine an individual’s genome to count how many depression-linked variants they carry. The more risk-related variants, the higher the person’s overall score. Some genetic variants contribute more strongly than others, allowing researchers to refine the prediction.
Twin data reveals how genes influence drug response
Polygenic risk scores don’t diagnose mental illnesses; instead, they estimate the likelihood of developing them. But they can also shed light on why some treatments work better for certain people.
To explore this, Åhs and his team applied PRS models to data from the Swedish Twin Registry — the largest twin database in the world. This resource enables scientists to separate the effects of genetics from those of the environment on health and behavior.
Because twins share nearly identical DNA, consistent patterns between them often point to genetic influences. In this study, Åhs identified 2,515 individuals from the registry who had been prescribed medications for depression or anxiety. By analyzing which drugs they used, whether they switched prescriptions, and how their treatments progressed, the researchers were able to infer which medications were most effective — and how genes might play a role in that response.
“We then looked at the polygenic risk scores of these individuals, and it became clear that if you had a higher risk score for depression or anxiety, drugs like benzodiazepine and histamines had a smaller effect,” Åhs says. “More research is needed, but hopefully, we’ll be able to develop accurate tests in the future that can predict which kind of drugs will most likely have an effect on you.”
Important limitations and next steps
As with most scientific studies, this research comes with certain limitations. Professor Åhs notes that although the dataset was large and comprehensive, it was not without flaws.
“The data on the patient’s response and nonresponse to different drugs was based on which drugs were prescribed to them, not clinical notes. We can infer a lot from the prescription data, but we can’t be sure if there was a slight bias,” he says.
“In other words, we don’t know exactly why they changed drugs. Was it because of side effects, lack of remission, or something else? We did compare our results with other studies that used clinical assessment, and they were consistent with ours.”
The researchers also had to limit their analysis to a defined time period, which means that some earlier medication records may not have been captured in the study..
“This might have influenced the number of individuals who received only one drug in our study. Some of them could have received other drugs before that weren’t registered in our data. That’s one of the reasons why we want to do a clinical follow-up study,” Åhs adds.
Toward personalized psychiatry
While further research is still needed, these findings point toward a future in which selecting an antidepressant may no longer rely on trial and error. A simple genetic test could one day enable doctors to identify the most effective medication for each patient from the very beginning – saving time, minimizing side effects, and improving the quality of life for millions worldwide.
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