Polygenic scores may identify long-term risk for depression, resilience

Multivariate polygenic score profiles may help identify long-term risk for depression and resilience, according to results of a longitudinal cohort study published in JAMA Psychiatry.

“Although the explanatory power of individual [polygenic scores] is modest, combining multiple [polygenic scores] has been applied to increase the predictive power for psychosocial outcomes,” Katharina Schultebraucks, PhD, of the department of emergency medicine at Columbia University Medical Center, and colleagues wrote. “Despite the utility of combining multiple [polygenic scores], to our knowledge, this approach has not yet been used to distinguish multinomial trajectory outcomes following adversity. Doing so can lead to several benefits, including a better understanding of the utility of genetic factors associated with resilience and enhancing the programmatic identification and allocation of clinical resources for those in need.”

Prior genomic studies of psychological resilience were limited because of their reliance on outcomes defined by cross-sectional designs, and prospective studies because of analyzing data from baseline and using a single follow-up.

To address this research gap, Schultebraucks and colleagues aimed to evaluate the discriminatory accuracy of a deep neural net that combined joint information from 21 polygenic scores related to psychiatry and health for distinguishing resilience from other longitudinal symptom trajectories by using longitudinal, genetically informed data of adults with major life stressor exposure. They analyzed data of 2,017 participants of the longitudinal panel cohort study the Health and Retirement Study who were aged older than 50 years, of European ancestry and had data available on depressive symptom trajectory information after having experienced an index depressogenic major life stressor. Major life stressors included bereavement, divorce and job loss or major health events, such as myocardial infarction and cancer.

Results showed 1,638 (79.1%) of participants were classified as resilient, 160 (7.75%) as improving, 159 (7.7%) as having emerging depression and 114 (5.5%) and having preexisting/chronic depression symptoms. The researchers reported high discriminatory accuracy of the deep neural nets for distinguishing these four trajectories, and it was higher for preexisting/chronic depression, followed by emerging depression, recovery and resilience.

“Because this data-driven study is inherently exploratory, external validation of the findings is an important next step and a prerequisite before the clinical use of the model is justified,” Schultebraucks and colleagues wrote. “Owing to the importance of accurately distinguishing between resilience and risk for emergent depressive symptoms following major life stressors, the presented approach to combine multiple [polygenic scores] using computational methods may be a useful approach for developing prognostic models that have potential to provide new areas for targeted interventions over time.”