Genomic prediction of depression risk and resilience under stress

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.

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Fig. 1: MDD-PRS distribution.
Fig. 2: Associations of MDD-PRS and PHQ-9 depressive symptom score and mediations of the associations by known risk factors.
Fig. 3: PHQ depression proportion by MDD-PRS group.

Data availability

The de-identified data from Intern Health Study are available through the PGC: https://www.med.unc.edu/pgc/shared-methods.

PGC phase 2–UK Biobank–23andMe MDD GWAS meta-analysis summary statistics: https://www.nature.com/articles/s41593-018-0326-7#data-availability.

Code availability

Custom code that supports the findings of this study is available from the corresponding author upon request.

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Acknowledgements

We thank the training physicians for taking part in this study. We also thank the participants and researchers of PGC and UK Biobank, and the research participants and employees of 23andMe, for making this work possible. This project was funded by the National Institute of Mental Health (grant no. R01MH101459 to S.S.). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

S.S. designed the study. S.S. and Y.F. developed the research question. Y.F. performed the data management and analysis. Y.F. and S.S. wrote the manuscript. L.S., P.S. and M.B. provided critical review, discussion and revision of the manuscript. All authors approved the manuscript.

Correspondence to Srijan Sen.

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Competing interests

The authors declare no competing interests.

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Peer review information Primary Handling Editor: Mary Elizabeth Sutherland.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

Associations of Imputed Data Derived MDD Polygenic Risk Score with PHQ-9 Depressive Symptom Scores (N = 5,227).

Extended Data Fig. 2 Baseline and Internship PHQ-9 Depressive Symptom Scores by MDD-PRS Group.

5,227 Subjects from Intern Health Study were binned into 40 groups of 2.5% of subjects (n=131 per group) from low to high MDD-PRS (left to right). The 40 x-axis groups are defined by group-wise average standardized MDD-PRS. Average PHQ-9 score of each group at baseline (cyan dots) and during internship (orange dots) are plotted with 95% CI error bars. LOESS fitting line (dash line) shadowed by 95% CI and linear regression fitting line (solid line) were applied to both baseline and internship plots. Optimal span parameter for LOESS regression was selected by generalized cross-validation method. Source data

Extended Data Fig. 3 Population Structure Based on the Top Two Principal Component (PC) Analysis of the Intern Health Study.

Blue, red and green boxes depicted the analysis inclusion range of European, South Asian and East Asian Groups. Source data

Supplementary information

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Fang, Y., Scott, L., Song, P. et al. Genomic prediction of depression risk and resilience under stress. Nat Hum Behav 4, 111–118 (2020). https://doi.org/10.1038/s41562-019-0759-3

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