Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder
Bibliographic record
Abstract
Background 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. 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 (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. Results 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). Conclusion 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".