Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder

Abstract

Polygenic scores (PGSs) hold the potential to identify patients who respond favourably to specific psychiatric treatments. However, their biological interpretation remains unclear. In this study, we developed pathway-specific PGSs (PS PGS ) for lithium response and assessed their association with clinical lithium response in patients with bipolar disorder. Using sets of genes involved in pathways affected by lithium, we developed nine PS PGSs and evaluated their associations with lithium response in the International Consortium on Lithium Genetics (ConLi + Gen: N=2367), validated in combined PsyCourse (N=105) and BipoLife (N=102) cohorts. The association between each PS PGS and lithium response — defined both as a continuous ALDA score and a categorical outcome (good vs poor responses) — was evaluated using regression models, adjusted for confounders. A significant association was determined after multiple testing correction at p<0.05. The PGS for acetylcholine, GABA and mitochondria were associated with response to lithium both in categorical and continuous outcomes. However, the PGS for calcium channel, circadian rhythm and GSK were associated only with the continuous outcome. Each score explained 0.29%–1.91% of variance in categorical and 0.30–1.54% in continuous outcomes. A multivariate modelling combining PS PGS that showed significant associations in the univariate analysis (combined PS PGS ), has increased the R 2 to 3.71% (categorical) and 3.18% (continuous) outcomes. Associations for PGSs for GABA and circadian rhythm were replicated. Patients with the highest genetic loading (10 th decile) for acetylcholine variants were 3.03 times more likely (95%CI: 1.95– 4.69) to show a good lithium response (categorical outcome) than those in the lowest (1 st decile). PS PGSs achieved predictive performance comparable to the conventional genome-wide PGSs, with the added advantage of biological interpretability using a smaller list of genetic variants.