A New Horizon in Depression Genetics: Enhancing the Predictive Power of Genetic Risk Scores

A groundbreaking study now published in Nature Genetics marks a turning point in depression genetics, offering new hope for personalized medicine through enhanced Polygenic Risk Scores (PRS).



Major Depressive Disorder (MDD) is a complex and debilitating condition affecting hundreds of millions worldwide. Its underlying biology has been notoriously indecipherable, making prediction and prevention a considerable challenge. MDD is a complex disorder that is the result of both genetic and environmental contributions; methods that leverage both large-scale genetic and phenotypic data to enhance MDD prediction is therefore high in demand.


A team co-led by PioneerCampus PI Na Cai and Dr Andrew Dahl from University of Chicago has made a remarkable progress, refining the precision and applicability of genetic risk assessments for this pervasive disorder. Na and her team demonstrated that it is possible to improve the power of genetic studies of MDD through leveraging vast amounts of phenotypic data available in biobanks, while ensuring the genetic findings are still specific to MDD. They employed cutting-edge statistical tools and machine learning techniques, and developed a new metric “PRS Pleiotropy”, which enables the assessment of specificity of polygenic risk scores (PRS, a measure of genetic risk of a disorder) to the disorder of interest.  This new metric, termed “PRS Pleiotropy”.


The innovative methods employed in the study not only increased the predictive accuracy and specificity of PRS for MDD in individuals of European ancestry, but also improved their portability across different ancestries, taking an important step towards closing a crucial equity gap in genetic research and precision medicine. Additionally, the new metric “PRS pleiotropy” is widely applicable to all phenotypes and disorders, aiding in the delicate balance between broad genetic influence and disease-specific risk. It is an advance that enables progress across all phenotypes and diseases.


The implications of this research extend beyond understanding MDD’s genetic basis; it signals a new era in which genetic risk prediction using data from large-scale biobanks and electronic health records have the potential to become more inclusive and customized to individual profiles. Such advancements in PRS are set to revolutionize the landscape of diagnostics and treatment strategies, paving the way for more effective, personalized interventions for all.

Link to publication