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Advanced Bayesian BMD-Derived Genome-Wide Polygenic Scores Enhance Clinical FRAX-Based Fracture Risk Prediction in
Anqi Liu1, Jianing Liu1, Qing Wu2
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
Calcified Tissue International
|March 24, 2026
Summary
Integrating advanced Bayesian genome-wide polygenic scores (GPS) into the Fracture Risk Assessment Tool (FRAX) improves fracture prediction accuracy. This enhanced Bayesian GPS-FRAX model offers better risk stratification, especially for older women near intervention thresholds.
Area of Science:
- Genetics and Genomics
- Epidemiology
- Biostatistics
Background:
- Current fracture prediction tools like FRAX lack genetic data, potentially causing misclassification due to osteoporosis's high heritability.
- This limitation is particularly significant for postmenopausal women near clinical intervention thresholds.
Purpose of the Study:
- To enhance clinical fracture risk prediction by integrating advanced Bayesian-derived genome-wide polygenic scores (GPS) into FRAX, creating the Bayesian GPS-FRAX model.
- To validate the model's performance in terms of discrimination and clinical utility in postmenopausal women.
Main Methods:
- Derived GPS using advanced Bayesian methods (PRS-CS, SBayesR) from GEFOS genetic data.
- Integrated Bayesian GPS into FRAX to form the Bayesian GPS-FRAX model.
- Validated the model in two cohorts (N=6932 and N=3688) using net reclassification improvement (NRI) and decision curve analysis.
Main Results:
- Bayesian GPS-FRAX modestly improved discrimination (AUC from 0.72 to 0.74).
- Achieved clinically meaningful risk classification gains (NRI of 4.55-5.07%) compared to FRAX-CRF.
- Successfully reclassified individuals near critical uncertainty thresholds (15-25% FRAX scores), with greatest benefit in women over 70.
Conclusions:
- Integrating Bayesian GPS into FRAX significantly enhances fracture risk stratification, particularly for older women and those near intervention thresholds.
- The Bayesian GPS-FRAX model represents an advancement in personalized osteoporosis management by addressing genomic complexity.
- Further prospective validation and cost-effectiveness studies are needed for clinical implementation.
