Millions worldwide suffer from depression and anxiety, often facing long and uncertain waits before finding the right medication. Now, scientists in Sweden, Denmark, and Germany are developing a genetic depression test that could predict which antidepressants will be most effective for each patient.
Depression Test Aims to Personalize Mental Health Treatment
Depression and anxiety rank among the world’s most common mental health conditions. Around 300 million people live with depression, and roughly 301 million with anxiety disorders—affecting nearly 8% of the global population.
Yet, finding effective treatment remains a challenge. Nearly half of all patients experience little or no improvement from their initial antidepressant prescription, forcing them to try multiple medications over weeks or months. This trial-and-error approach delays recovery and increases emotional distress.
A New Genetic Depression Test Using Polygenic Risk Scores
Researchers from Germany, Sweden, and Denmark believe genetics could hold the key to solving this problem. They are pioneering an approach using polygenic risk scores (PRS)—a tool that analyzes variations in a person’s DNA to predict how they might respond to specific antidepressants or anti-anxiety medications.
With a single genetic test, doctors could one day estimate which drug is most likely to work for an individual patient. Although the method has so far been tested only on genetic research databases rather than real-world patients, early results are encouraging.
Professor Fredrik Åhs from the Department of Psychology and Social Work at Mid Sweden University, who leads the study, hopes clinical trials will soon begin.
“We believe this technology could help doctors select the right medicine more quickly,” Åhs explains. “Our goal is to create a test that identifies effective drugs based on genetics—and possibly biomarkers—so patients can receive relief much faster.”
Building on Research from Aarhus University in Denmark
The project began when Åhs collaborated with Professor Doug Speed of the Center for Quantitative Genetics and Genomics at Aarhus University. Speed’s expertise in polygenic modeling has been instrumental in refining methods to analyze complex genetic data related to psychiatric disorders.
“For the past decade, we’ve worked to predict disease risk using polygenic scores,” Speed says. “What’s remarkable is that these same scores can also predict how someone might respond to drugs—a surprising but exciting development.”
His models cover conditions including schizophrenia, anxiety, bipolar disorder, and depression, forming the foundation for this new depression test research.
Understanding Polygenic Risk Scores in the Depression Test
Since the mapping of the human genome, scientists have identified thousands of genetic variations influencing health outcomes. Every person has roughly 20,000 genes, many of which exist in multiple forms or alleles. Some alleles increase vulnerability to diseases such as depression.
Polygenic risk scores combine the influence of these genetic variants to estimate an individual’s risk for developing a condition. For depression, the more risk-linked variants a person carries, the higher their PRS—and potentially, the more their genetic profile influences how they respond to antidepressants.
Twin Data Strengthens Genetic Link in Depression Test Study
To test their theory, Åhs and his team analyzed data from the Swedish Twin Registry, the world’s largest twin database. This allowed them to compare how genetic similarities influence antidepressant effectiveness.
They identified 2,515 individuals who had been prescribed antidepressants or anti-anxiety medications. By tracking which drugs were used, switched, or discontinued, the researchers could infer which treatments were most effective.
“Our analysis showed that people with higher genetic risk scores for depression or anxiety responded less effectively to drugs like benzodiazepines and histamines,” Åhs notes. “While more research is needed, these results suggest that polygenic data could guide personalized treatment choices.”
Limitations of the Current Depression Test Research
Despite promising findings, Åhs acknowledges limitations. The data used were based on prescription records, not direct clinical assessments.
“We can infer much from prescription patterns, but we don’t always know why a drug was changed—whether due to side effects or lack of efficacy,” he explains.
Additionally, the study’s time frame excluded some earlier prescriptions, possibly omitting relevant treatment history. Åhs and his colleagues plan clinical follow-up studies to verify their findings in real-world settings.
Conclusion
The implications of this research are profound. In the future, choosing an antidepressant may no longer depend on guesswork. A simple genetic depression test could match patients with the most effective medicat
ion from the start—saving time, minimizing side effects, and improving recovery outcomes for millions worldwide.
If successful, this approach could mark a transformative step toward personalized psychiatry, where treatment decisions are guided by genetics rather than trial and error.
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