Prospective Study of Polygenic Risk, Protective Factors, and Incident Depression Following Combat Deployment in US Army Soldiers
Robert J Ursano,
Ronald C Kessler,
Karestan C. Koenen,
Gary H. Wynn,
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Laura Campbell-Sills,
Murray B. Stein,
Jordan W Smoller
Posted 06 Jul 2018
bioRxiv DOI: 10.1101/361725 (published DOI: 10.1017/S0033291719000527)
Posted 06 Jul 2018
Background: Whereas genetic susceptibility increases risk for major depressive disorder (MDD), non-genetic protective factors may mitigate this risk. In a large-scale prospective study of US Army soldiers, we examined whether trait resilience and/or unit cohesion could buffer against the onset of MDD following combat deployment, even in soldiers at high polygenic risk. Methods: Data were analyzed from 4,182 soldiers of European ancestry assessed before and after their deployment to Afghanistan. Incident MDD was defined as no MDD episode at pre-deployment, followed by a MDD episode following deployment. Polygenic risk scores were constructed from the largest available MDD genome-wide association study. We first examined main effects of the MDD PRS and each protective factor on incident MDD. We then tested effects of each protective factor on incident MDD across strata of polygenic risk. Results: Polygenic risk showed a dose-response relationship to depression, such that soldiers at high polygenic risk had greatest odds for incident MDD. Both unit cohesion and trait resilience were prospectively associated with reduced risk for incident MDD. Notably, the protective effect of unit cohesion persisted even in soldiers at highest polygenic risk. Conclusions: Polygenic risk was associated with new-onset MDD in deployed soldiers. However, unit cohesion - an index of perceived support and morale - was protective against incident MDD even among those at highest genetic risk, and may represent a potent target for promoting resilience in vulnerable soldiers. Findings illustrate the value of combining genomic and environmental data in a prospective design to identify robust protective factors for mental health.
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