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. 2020 Jan;4(1):111-118.
doi: 10.1038/s41562-019-0759-3. Epub 2019 Oct 28.

Genomic Prediction of Depression Risk and Resilience Under Stress

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Free PMC article

Genomic Prediction of Depression Risk and Resilience Under Stress

Yu Fang et al. Nat Hum Behav. .
Free PMC article

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.

Conflict of interest statement

Competing Interests Statement

The authors declare no competing interests.

Figures

Extended Data Fig. 1
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
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.
Extended Data Fig. 3
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.
Figure 1.
Figure 1.. MDD-PRS Distribution.
MDD-PRS has a near-normal distribution in Intern Health Study samples (n = 5,227). Represented on the x-axis, MDD-PRS was mean-centered and scaled to a standard deviation of 1.
Figure 2.
Figure 2.. Associations of MDD-PRS and PHQ-9 Depressive Symptom Score and Mediations of the Associations by Known Risk Factors.
a) - c) The associations of MDD-PRS and PHQ-9 score at baseline, during internship and change, without mediator (left diagram) and mediated by known baseline risk factors (right diagram) (In a, β1 = 0.068, β2 = 0.159, γ = 0.353, all three p < 2 × 10−16; β3 = 0.012, p = 0.32; in b, β1 = 0.093, β2 = 0.159, γ = 0.340, all three p < 2 × 10−16; β3 = 0.039, p = 1.26 × 10−4; in c, β1 = 0.058, β2 = 0.159, γ = 0.122, all three p < 2 × 10−16; β3 = 0.039, p =0.005); d) Percentage of variance in first principal component explained by each risk factor).
Figure 3.
Figure 3.. PHQ Depression Proportion 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. The proportion of subjects meeting criteria for PHQ depression 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 logistic 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.

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