Genomic prediction of depression risk and resilience under stress

Yu Fang, Laura Scott, Peter Song, Margit Burmeister and Srijan Sen ()
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Yu Fang: University of Michigan
Laura Scott: University of Michigan
Peter Song: University of Michigan
Margit Burmeister: University of Michigan
Srijan Sen: University of Michigan

Nature Human Behaviour, 2020, vol. 4, issue 1, 111-118

Abstract: Abstract Advancing ability to predict who is likely to develop depression holds great potential in reducing the disease burden. Here, we use the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the major depressive disorder polygenic risk score (MDD-PRS) derived from the most recent Psychiatric Genomics Consortium–UK Biobank–23andMe genome-wide association study to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (β = 0.095, P = 4.7 × 10−16) and that MDD-PRS was more strongly associated with depression under stress than at baseline (MDD-PRS × stress interaction β = 0.036, P = 0.005). Further, known risk factors accounted for substantially less of the association between MDD-PRS and depression when under stress than at baseline, suggesting that MDD-PRS adds unique predictive power in depression prediction. Finally, we found that low MDD-PRS may have particular use in identifying individuals with high resilience. Together, these findings suggest that MDD-PRS holds promise in furthering our ability to predict vulnerability and resilience under stress.

Date: 2020
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DOI: 10.1038/s41562-019-0759-3

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