Genetic markers show promise in predicting mood disorder treatment outcomes

In a comprehensive Genomic Press Thought Leaders Invited Review, researchers have synthesized findings from dozens of studies examining how genetic markers for mood disorders influence treatment outcomes and clinical features across major psychiatric conditions. The analysis reveals that while polygenic scores currently show modest predictive power, they demonstrate consistent patterns that could eventually contribute to more personalized psychiatric care.

Genetic signatures show consistent treatment patterns

Professor Alessandro Serretti from Kore University of Enna analyzed evidence spanning from 2013 to 2025, examining how polygenic scores for major depressive disorder (MDD) and bipolar disorder (BD) relate to treatment outcomes. These scores aggregate the effects of hundreds to thousands of common genetic variants into a single measure of genetic liability for psychiatric conditions.

The review found that higher polygenic scores for depression consistently correlate with poorer treatment outcomes across multiple disorders. Patients with elevated genetic risk for depression showed increased likelihood of nonresponse to antidepressants, mood stabilizers, antipsychotics, lower remission rates, and greater treatment resistance in major depression, bipolar disorder and schizophrenia. This pattern held across diverse populations and treatment approaches, suggesting a genuine biological relationship rather than statistical artifact.

The majority of studies point to a modest but consistent relationship between MDD polygenic scores and antidepressant treatment outcomes. Higher polygenic load for depression correlates with a greater likelihood of nonresponse, nonremission, or resistance to conventional antidepressant therapies."

Professor Alessandro Serretti from Kore University of Enna 

Moreover, the detrimental effect was observed, though with a less strong evidence, also for bipolar disorder and schizophrenia treatment outcome.

Bipolar genetics reveal complex treatment relationships

Polygenic scores for bipolar disorder showed more nuanced effects. While these markers demonstrated limited predictive value for antidepressant response in unipolar depression, they revealed intriguing patterns in bipolar disorder treatment. On one hand, it can associate with better educational outcomes or higher cognitive functioning; on the other, it may predispose to psychotic dimensions in specific contexts.

Such evidence highlights how BD genetic liability does not uniformly confer negative outcomes and may, in some contexts, be advantageous.

Environmental interactions add clinical complexity

The analysis uncovered compelling evidence that genetic risk for mood disorders interacts with environmental factors. Studies consistently showed that individuals with higher genetic risk for depression reported greater exposure to stressful life events and demonstrated heightened vulnerability to adverse environmental conditions.

Conversely, genetic risk for bipolar disorder sometimes associated with positive outcomes, including higher educational attainment and better cognitive performance in certain populations. This duality reflects the complex pleiotropy of psychiatric genetics, where the same genetic variants may confer both risks and advantages depending on context.

Could these gene-environment interactions explain why some patients with similar genetic profiles experience vastly different clinical trajectories? The research suggests that genetic liability may influence not only direct disease risk but also the likelihood of encountering environmental stressors that further modify outcomes.

Clinical implementation remains premature

Despite consistent findings, the clinical utility of current polygenic scores remains limited. Even when statistically significant, these genetic markers typically explain less than 1% of variance in treatment outcomes. This modest effect size reflects the persistent "missing heritability" challenge in psychiatric genomics, where identified variants account for only a fraction of the genetic influence on complex traits.

Professor Serretti emphasized that polygenic scores should currently be viewed as incremental predictive markers rather than clinical decision tools. "While these scores show promise, their additional explanatory power beyond conventional clinical predictors often remains marginal," he noted.

Ancestry gaps limit global applicability

The review highlighted a critical limitation in current research: most genome-wide association studies underlying these polygenic scores have been conducted in populations of European ancestry. This creates significant challenges for implementing genetic prediction in diverse populations worldwide.

Recent studies in Asian populations, particularly Han Chinese samples, have shown broadly consistent directions of effect for depression polygenic scores. However, differences in genetic architecture across populations may substantially alter predictive accuracy when scores derived from European samples are applied elsewhere.

How might expanding genetic research to include diverse ancestries change our understanding of psychiatric genetics? The answer could determine whether polygenic approaches achieve their potential for global mental health improvement once large and ethnically heterogeneous studies will be available.

Machine learning offers enhanced prediction

Emerging approaches that combine polygenic scores with clinical data using machine learning techniques show more substantial improvements in outcome prediction. Some studies achieved variance explanations of 4-5% when integrating genetic and clinical information, compared to 1-2% for genetic markers alone.

These integrative models represent a promising direction for translating genetic liability into clinically actionable insights. Rather than relying on genetics in isolation, future applications may leverage comprehensive risk profiles that include biological, clinical, and environmental factors.

Future directions point toward precision psychiatry

The synthesis reveals several promising research directions. Ongoing genome-wide association studies with larger sample sizes and improved statistical methods are enhancing the accuracy of polygenic scores. Additionally, researchers are developing more sophisticated approaches that account for the heterogeneity within psychiatric diagnoses.

Integration with neurophysiological measures, such as electroencephalogram biomarkers, and investigation of gene-by-environment interactions offer additional avenues for improving prediction. Some studies have begun exploring how genetic risk modulates the impact of childhood trauma, stress exposure, and other environmental factors on psychiatric outcomes.

Could the next generation of polygenic scores incorporate epigenetic modifications, rare genetic variants, or dynamic environmental exposures? These developments might bridge the gap between current modest predictions and clinically meaningful guidance for treatment selection.

Implications for mental health care

While immediate clinical implementation remains premature, this research establishes important foundations for future precision psychiatry approaches. The consistent patterns observed across studies suggest that genetic factors genuinely influence treatment response, even if current measurements capture only a fraction of this influence.

The findings also highlight the importance of considering environmental factors alongside genetic risk. Patients with high genetic liability for depression may benefit from more intensive environmental interventions or enhanced monitoring for stress-related symptom exacerbation.

As polygenic prediction improves, these tools might eventually support clinical decision-making through risk stratification or treatment selection. However, such applications will require substantial additional research, including randomized controlled trials demonstrating clinical utility and cost-effectiveness.

The review underscores both the promise and current limitations of genetic approaches to psychiatric treatment prediction. While polygenic scores for mood disorders show consistent associations with clinical outcomes, translating these findings into routine clinical practice requires continued methodological advances, ancestral diversity expansion, and integration with comprehensive clinical assessment.