Genomic prediction of depression risk and resilience under stress
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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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The genetic risk of depression is likely due to small effects of hundreds or thousands of common genetic variants, with overrepresentation of genes affecting brain development, inflammation, bioenergetics and neuronal signalling.
“The genetic risk of depression is likely due to small effects of hundreds or thousands of common genetic variants, with overrepresentation of genes affecting brain development, inflammation, bioenergetics and neuronal signalling.”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [264]
Every individual carries some risk variants for depression, and a polygenic score predicts the genetic liability to depression better than any specific genetic variant.
“The large number of common variants involved 262-264 entails that every individual carries some risk variants for depression, and a polygenic score (indexing the individual's load of risk variants) predicts the genetic liability to depression better than any specific genetic variant. 265”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [264]
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- Authors
- Fang, Y., Scott, L., Song, P., Burmeister, M., & Sen, S.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Sen, Srijan
- Venue
- Nature Human Behaviour
- Published
- 2019-10-28
- Volume
- 4
- Issue
- 1
- Pages
- 111-118
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC6980948
- OpenAlex
- W2981752943
- Cited by
- 43
- References
- 41
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 4 · 2023: 10 · 2022: 8 · 2021: 9 · 2020: 2
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH101459)
- American Foundation for Suicide Prevention
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- depression, polygenic risk score, major depressive disorder, genomic prediction, stressor interactions, psychiatric genetics, depression risk prediction
- MeSH terms
- Adult, Major Depressive Disorder, Education, Medical, Female, Humans, Male, Physicians, Prognosis, Stress, Psychological, Databases, Factual, Multifactorial Inheritance, Genome-Wide Association Study, Resilience, Psychological, Gene-Environment Interaction
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