A simple DNA test could reveal the right antidepressant for you

Yet for many, finding the right treatment can be a slow and frustrating process. When patients first seek help, the medications they’re prescribed often fail to work. In fact, nearly half of all people treated for depression or anxiety experience little or no benefit from their initial prescription, forcing them to try multiple drugs over weeks or even months before finding relief.

A New Genetic Approach to Treatment

A research team from Germany, Sweden, and Denmark believes they may have found a solution. They’ve developed a genetic approach that could help doctors predict which antidepressant or anti-anxiety medication will actually work for a specific person.

The method uses what are known as polygenic risk scores (PRS), which analyze a person’s DNA to assess how their genetic variations might influence their response to certain medications. With just one genetic test, scientists can now estimate which drugs are most likely to help an individual patient.

Although this technique has so far only been tested using genetic research databases rather than real patients, the results are promising.

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 soon.

“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 began two years ago when Åhs reached out to Professor Doug Speed from the Center for Quantitative Genetics and Genomics at Aarhus University in Denmark. Åhs wanted to apply Speed’s advanced polygenic risk score models to his research on mental health treatment.

Speed has spent years refining ways to analyze complex human genetic data, focusing especially on how genes influence psychological conditions.

“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 PRS models for several psychiatric disorders, including schizophrenia, anxiety, bipolar disorder, and depression — all of which were used in this new study.

What Are Polygenic Risk Scores?

Since the mapping of the human genome in the early 2000s, scientists have discovered thousands of small variations in DNA that can influence health. Humans have roughly 20,000 genes, and each comes in multiple versions (known as alleles). Some alleles are linked to a higher likelihood of developing specific diseases.

Researchers like Speed compile this information to create polygenic risk scores — tools that combine the effects of many genetic variations to estimate a person’s risk for certain conditions.

When developing a PRS for depression, for example, researchers analyze a person’s genome to see how many depression-linked variants they carry. The more risk-associated variations present, the higher the individual’s genetic risk score. Some variants, however, have a stronger influence than others.

Twin Data Reveals How Genes Affect Drug Response

Polygenic risk scores don’t diagnose mental illnesses. Instead, they estimate the likelihood of developing one. But they may also help explain why some treatments work better for certain people.

To explore this, Åhs and his team applied polygenic risk scores to data from the Swedish Twin Registry, the largest of its kind in the world. This database allows researchers to compare the relative effects of genetics and environment on health and behavior.

Because twins share nearly identical DNA, patterns that appear consistently across twin pairs often point to genetic causes. Åhs identified 2,515 individuals from the registry who had been prescribed medications for depression or anxiety. By examining which drugs they used, whether they switched prescriptions, and how their treatment progressed, the researchers could infer which medications were most effective.

“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

Like most scientific research, the study has some limitations. Åhs explains that while the dataset was extensive, it wasn’t perfect.

“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 restrict their analysis to a specific time window, meaning some earlier prescriptions may not have been included.

“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

Although more research is needed, the findings suggest a future where choosing an antidepressant might no longer depend on trial and error. A simple genetic test could one day help doctors match patients with the most effective treatment from the start — potentially saving time, reducing side effects, and improving lives for millions around the world.