Psychological MedicineGeneticsPeer-reviewed journal article
Using Major Depression Polygenic Risk Scores to Explore the Depressive Symptom Continuum
Bradley S. JermyiD, Saskia P. HagenaarsiD, Kylie P. GlanvilleiD, Jonathan R. I. ColemaniD, David M. HowardiD, Gerome D. BreeniD, Evangelos VassosiD, Cathryn M. LewisiD
Abstract
BackgroundIt is not clear whether major depression (MD) is a categorical disorder or if depressive symptoms exist on a continuum based on severity. Observational studies comparing sub-threshold and clinical depression suggest MD is continuous, but many do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of MD. MethodsFactor analysis on symptom-level data from the UK Biobank (N=148,957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores for a categorical definition of MD (N=119,692). ResultsConfirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised {beta} range: 0.057 - 0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (p-value range: 2.28x10-3 - 4.56x10-7). ConclusionsAn association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 17
- FWCI
- 1.46
- Field percentile
- top 19%
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