P117. Predicting Genetic Risk for Depression and Anxiety DisordersShow others and affiliations
2022 (English)In: Biological Psychiatry, ISSN 0006-3223, E-ISSN 1873-2402, Vol. 91Article in journal, Meeting abstract (Refereed) Published
Abstract [en]
Background
Polygenic scores (PGSs) harness the potential to provide an overall measure of individuals’ genetic liability to develop a disease (Torkamani et al., 2018), though much research is still needed. The aim of the present study was to predict prescription of pharmacological treatment of anxiety or depression from PGSs.
Methods
The target sample comprised two cohorts of genotyped Swedish twins (n = 11037). Cases were defined as individuals prescribed pharmacological treatment of depression (n = 1129) or anxiety (n = 1446). We constructed 6 PGSs based on GWAS on MDD diagnosis, Anxiety, Schizophrenia, Neuroticism scores, the GAD-7 scale, and the PHQ-9. Data were analyzed by logistic regression models with change in pseudo-R2 (above the baseline model with sex, age, cohort, and 20 ancestral PCs) following the inclusion of PGSs to predict the risk of anxiety or depression medication. All results corrected for multiple comparisons.
Results
Predictive performance was estimated to ΔR2depression = 0.028; ΔR2anxiety = 0.025 when all PGSs were included in the same model, with PGS for MDD being the single best predictor for both anxiety and depression. Individuals in the top 10% of the PGS distribution had greater odds of drug prescription (ORdepression = 1.82; CI95% = 1.53—2.17; ORanxiety = 1.65; CI95% = 1.40—1.95), while the bottom 10% had decreased risk (ORanxiety = 0.56; CI95% = 0.45—0.70; ORdepression = 0.58; CI95% = 0.45—0.74) compared to the remaining 90% of the distribution.
Conclusions
PGSs can predict drug prescription for anxiety and depression in an independent sample.
Place, publisher, year, edition, pages
2022. Vol. 91
National Category
Psychiatry Medical Genetics and Genomics
Identifiers
URN: urn:nbn:se:miun:diva-46884DOI: 10.1016/j.biopsych.2022.02.351OAI: oai:DiVA.org:miun-46884DiVA, id: diva2:1728471
2023-01-182023-01-182025-02-10Bibliographically approved