Genomic Prediction of Depression Risk and Resilience Under Stress

Advancing ability to predict who is likely to develop depression holds great potential in reducing the disease burden. Here, we utilize the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the depression polygenic risk score (MDD-PRS) derived from the most recent PGC2/UKB/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.095, p=4.7×10(−16)) and that MDD-PRS was more strongly associated with depression under stress than at baseline (MDD-PRSxstress interaction beta=0.036, p=0.005). Further, known risk factors accounted for substantially less of the association between MDD-PRS and depression at 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 utility 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.