Using Major Depression Polygenic Risk Scores to Explore the Depressive Symptom Continuum

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 ß 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: 2.28×10−3 - 4.56×10−7).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.

Age and sex‐related variability in the presentation of generalized anxiety and depression symptoms

“…Importantly, we replicated the generalized anxiety and depression factor structure identified in the UK Biobank (Jermy et al, 2020) in a separate, large, and clinically relevant cohort. Thus, our findings demonstrate the need to address the appropriateness of current diagnostic classifications.…”

“…They also lack information on which symptoms are present for an individual and how those symptoms interact (Jokela et al, 2019). For instance, four underlying factors of generalized anxiety and depressive symptoms were identified in the UK Biobank: anxiety symptoms, psychomotor‐cognitive impairment, neurovegetative states, and mood symptoms (Jermy et al, 2020). Exploring patterns of symptom co‐occurrence instead of using sum scores may identify diagnostic subtypes of MDD, GAD, or their comorbid presentation, helping to refine diagnoses (Eeden et al, 2019).…”

What’s in a diagnosis? A genetic decomposition of major depression

“…Participants in the final sample were unrelated and of European ancestries which were identified using a previously described analytical pipeline (Supplementary Methods) (21,26,27). A total of 560,173 genotyped and 9,940,918 imputed SNPs remained after QC.…”

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

“…Recent studies have investigated genetic contributions to individual depressive symptoms ( 83–86 ) and how they vary across contexts ( 87 ). Analyses have shown that genetic contributions to individual symptoms are not equivalent to those for MDD (average rG = 0.6), nor to each other (rG range 0.6–0.9) ( 56 , 62 ). Going beyond symptoms, recent expansions in sequencing and phenotyping technologies such as neuroimaging ( 88–90 ) and molecular data ( 91 , 92 ) have enabled genetic analyses on endophenotypes ( 93 ).…”

“…Despite these challenges, there are increasing efforts to recover latent dimensions and classes at the genetic level. Building on the identification of three genetic factors reflecting mood, psychomotor/cognitive and neurovegetative features of MDD using twin modeling ( 55 ), a recent EFA on self-reported depression symptoms in UKBiobank obtained similar results and explored associations with depression PRS ( 56 ). A new framework, GenomicSEM, generalizes the structural equation modeling (SEM) approach to genetic covariance matrices ( 57 ), which can be generated from a joint analysis of GWAS summary statistics of individual depression symptoms, and can be used to test for genetic loadings on latent dimensions of depression.…”