Abstract
Background
Polygenic scores (PGSs) hold the potential to identify patients who respond favourably to specific psychiatric treatments. However, their biological interpretations remain 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 (BD).
Methods
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 cohort (ConLi + Gen: N = 2367), validated in the combined PsyCourse (N = 105) and BipoLife (N = 102) cohorts. Lithium responsiveness was assessed using the Retrospective Assessment of the Lithium Response Phenotype Scale (ALDA scale), for categorical outcome (good vs poor response) and continuous ALDA total score. Logistic and linear regressions, adjusting for age, sex, chip type, and the first four genetic principal components, were used to test associations, after multiple testing corrections ( p <0.05).
Results
Response to lithium was associated with PS PGS for acetylcholine, GABA, calcium channel signalling, mitochondria, circadian rhythm, and GSK pathways, R² ranging from 0.29% to 1.91%, with R² of 3.71% for the combined PS PGS. Associations for GABA PGS and CIR PGS were replicated. In decile-based stratified analysis, patients with the highest genetic loading (10 th decile) for acetylcholine pathway genetic variants were 3.03 times (95%CI: 1.95 – 4.69) more likely to have a good lithium response than the lowest decile (1 st decile).
Conclusion
PS PGSs achieved predictive performance comparable with conventional genome-wide PGSs, with more biological interpretability and using a smaller list of genetic variants, facilitating further investigation into the interaction of variants and biological pathways underlying lithium response.
Full Text Availability
The license terms selected by the author(s) for this preprint version do not permit archiving in PMC. The full text is available from the preprint server.