European Psychiatry (Jan 2022)

Comparing personalized brain-based and genetic risk scores for major depressive disorder in large population samples of adults and adolescents

  • Gladi Thng,
  • Xueyi Shen,
  • Aleks Stolicyn,
  • Mathew A. Harris,
  • Mark J. Adams,
  • Miruna C. Barbu,
  • Alex S. F. Kwong,
  • Sophia Frangou,
  • Stephen M. Lawrie,
  • Andrew M. McIntosh,
  • Liana Romaniuk,
  • Heather C. Whalley

DOI
https://doi.org/10.1192/j.eurpsy.2022.2301
Journal volume & issue
Vol. 65

Abstract

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Abstract Background Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based ‘Regional Vulnerability Index’ (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample (N = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline N = 3,825, age = 10 ± 1; 2-year follow-up N = 2,081, age = 12 ± 1). Methods MDD-RVIs quantify the correlation of the individual’s corresponding brain metric with the expected pattern for MDD derived in an independent sample. Using the same methodology across samples, subject-specific MDD-PRS and six MDD-RVIs based on different brain modalities (subcortical volume, cortical thickness, cortical surface area, mean diffusivity, fractional anisotropy, and multimodal) were computed. Results In adults, MDD-RVIs (based on white matter and multimodal measures) were more strongly associated with MDD (β = 0.099–0.281, PFDR = 0.001–0.043) than MDD-PRS (β = 0.056–0.152, PFDR = 0.140–0.140). In adolescents, depressive symptoms were associated with MDD-PRS at baseline and follow-up (β = 0.084–0.086, p = 1.38 × 10−4−4.77 × 10−4) but not with any MDD-RVIs (β 0.05). Conclusions Our results potentially indicate the ability of brain-based risk scores to capture a broader range of risk exposures than genetic risk scores in adults and are also useful in helping us to understand the temporal origins of depression-related brain features. Longitudinal data, specific to the developmental period and on white matter measures, will be useful in informing risk for subsequent psychiatric illness.

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