A multi-trait approach improves polygenic risk scores for chronic back pain across population-based and clinically ascertained samples
Bibliographic record
Abstract
Abstract Chronic back pain (CBP) is a complex, heritable condition, and a leading cause of global disability. Previous genome-wide (GW) CBP polygenic risk scores (PRS) derived from a large-scale cohort have shown low discrimination without clinical validation. To improve PRS performance and clinical relevance, we applied Multi-Trait Analysis of GWAS (MTAG) to summary statistics from five genetically correlated traits of European-ancestry individuals with UK Biobank (UKB) CBP as the primary trait, including dorsalgia and chronic musculoskeletal pain (N(effective)=492,717). For comparison, we also constructed a single-trait PRS using UK CBP-only GW data (N=234,013). PRS construction parameters were optimized in an independent large-scale cohort, the Canadian Longitudinal Study on Aging (CLSA) via five-fold cross-validation using LD clumping and p-value thresholding. With covariate adjustment, the MTAG-PRS achieved an AUC of 0.603 (AUC = 0.621; AUPRC = 0.346; R² = 0.051) that was slightly better than the UKB-only PRS (AUC = 0.604; AUPRC = 0.330; R² = 0.038). External validation in CBP cases and controls from another large-scale cohort CARTaGENE) confirmed the MTAG-PRS robustness (AUC = 0.638; AUPRC = 0.335; R² = 0.064). Validation in clinician-ascertained CBP cases (GENE-PAR study) contrasted against an independent subset of CARTaGENE controls improved the MTAG-PRS performance beyond the threshold for clinical utility (AUC = 0.785; AUPRC = 0.616; R² = 0.306). GENE-PAR CBP cases in the top decile PRS also displayed greater burden of CBP symptoms. These findings demonstrate that leveraging genetic pleiotropy, coupled with rigorous phenotyping, moved CBP PRS to clinical utility.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".