2026 ECNP Congress

PS02-2131

Associations between transdiagnostic polygenic liability and antidepressant outcomes in major depressive disorder

Giovanni Cantarella — L. Pelizza, S. Kasper, J. Zohar, D. Souery, S. Montgomery, D. Albani, P. Ferentinos, D. Rujescu, J. Mendlewicz, A. Serretti, A.R. Atti, A.M. Monteleone, L. Grassi, P. MNESYS - Mood and Psychosis Spoke, M. Menchetti, D. De Ronchi, C. Fabbri, G. Fanelli · Alma Mater Studiorum – Università di Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy

2026 ECNP Congress

Psychiatric disorders share substantial genetic architecture, with many single nucleotide polymorphisms (SNPs) contributing risk across multiple disorders. Recently, the use of Genomic Structural Equation Modelling (SEM) has modelled this cross-disorder genetic liability identifying four latent factors across 11 psychiatric disorders[1]. Transcriptome-wide SEM extended this approach by integrating gene expression, yielding five transdiagnostic latent genomic factors linked to 13 disorders[2]. In the present study, we tested whether polygenic liability indexed by these five SEM latent factors - compulsive, thought disorders, neurodevelopmental, internalising, and substance use - is associated with treatment outcomes in major depressive disorder (MDD).

The genome-wide summary statistics of these latent factors were quality controlled, retaining only common (minor allele frequency ≥ 0.01) and well-imputed variants (INFO ≥ 0.9). Posterior SNP effect sizes (weights) were obtained with PRS-cs[3]. Individual-level factor-based polygenic risk scores (PGSs) were then calculated with PRS-cs in the target cohort, that consisted in adults (N= 1059) with MDD recruited by the European Group for the Study of Resistant Depression (GSRD)[4]. We examined the following outcomes: a) remission, defined as MADRS ≤9 at follow-up; b) treatment response vs non-response after an adequate antidepressant trial, defining response as a ≥ 50% reduction in MADRS total and a current MADRS score <22; c) treatment resistance, defined as non-response to ≥ 2 consecutive adequate antidepressant trials; d) augmentation with antipsychotics or mood stabilizers during the index treatment period as a marker of treatment complexity. Associations between PGSs for each latent factor and treatment outcomes were tested using logistic regression, adjusting for age, sex, baseline symptom severity (MADRS scores), recruitment sites, and nine population principal components. Since we hypothesised that PGS effects might vary with baseline severity, we also included pre-specified interaction terms between each PGS and baseline MADRS in the analyses. Multiple testing was controlled with the Benjamini–Hochberg false discovery rate[5], considering four simultaneous tests (one per outcome); significance was set at q < 0.10 (two-sided).

We found that a higher PGS for the compulsive disorder factor was associated with lower rates of symptomatic remission (OR=0.29[95% CI= 0.09-0.93], p=0.038, q=0.038), and higher odds of developing non-response (OR=3.28[1.26-8.67], p=0.0155, q=0.025) and treatment resistance (OR=3.95[1.25-12.49], p=0.019, q=0.031). For antipsychotic augmentation, the PGS for internalising symptoms interacted with baseline symptom severity (OR=0.97[0.95–0.99], p= 0.005, q=0.020): post-hoc tests showed that higher PGSs were associated with greater odds of augmentation at low MADRS (25th percentile: slope=0.38; p= 0.0098), while the effect became non-significant at higher MADRS scores (e.g., 50th and 90th percentiles, p ≥ 0.18). We did not find any association when considering the other PRSs (all p ≥ .05).

In summary, these results show that transdiagnostic genetic liability measured through PGS can relate to treatment outcomes in MDD, with effects that vary by baseline symptom severity. Replication in independent cohorts and prospective testing should assess their utility for stratified trial design and baseline-informed treatment planning.

References

  1. Grotzinger, A. D., Mallard, T. T., Akingbuwa, W. A., Ip, H. F., Adams, M. J., Lewis, C. M., McIntosh, A. M., Grove, J., Dalsgaard, S., Lesch, K. P., Strom, N., Meier, S. M., Mattheisen, M., Børglum, A. D., Mors, O., Breen, G., iPSYCH, Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the Psychiatric Genetics Consortium, Bipolar Disorder Working Group of the Psychiatric Genetics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genetics Consortium, … Nivard, M. G. (2022). Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nature genetics, 54(5), 548–559.
  2. Grotzinger, A. D., Singh, K., Miller-Fleming, T. W., Lam, M., Mallard, T. T., Chen, Y., Liu, Z., Ge, T., & Smoller, J. W. (2023). Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing. JAMA psychiatry, 80(8), 811–821.
  3. Ge, T., Chen, C. Y., Ni, Y., Feng, Y. A., & Smoller, J. W. (2019). Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature communications, 10(1), 1776.
  4. Souery, D., Oswald, P., Massat, I., Bailer, U., Bollen, J., Demyttenaere, K., Kasper, S., Lecrubier, Y., Montgomery, S., Serretti, A., Zohar, J., Mendlewicz, J., & Group for the Study of Resistant Depression (2007). Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. The Journal of clinical psychiatry, 68(7), 1062–1070.
  5. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300.