A multi-trait approach improves polygenic risk scores for chronic back pain across population-based and clinically ascertained samples

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.