Pathway-Specific Polygenic Scores for Lithium Response for Predicting Clinical Lithium Treatment Response in Patients with Bipolar Disorder
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".