The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women


The ability of the fracture risk assessment tool (FRAX) in discriminating fracture and non-fracture in postmenopausal women remains suboptimal. Adding a genetic profile may improve the performance of FRAX. Three genetic risk scores (GRSs) (GRS_fracture, GRS_BMD, GRS_eBMD) were calculated for each participant in the Women’s Health Initiative Study (n = 23,981), based on the summary statistics of three comprehensive osteoporosis-related genome-wide association studies (GWAS). The primary outcomes were incident major osteoporotic fracture (MOF) and hip fracture (HF). The association between each GRS and fracture risk were evaluated in separate Cox Proportional Hazard models, with FRAX clinical risk factors adjusted for. The discrimination ability of each model was assessed using Area Under the Curve (AUC). The predictive improvement attributable to each GRSs was assessed using the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). GRS_BMD and GRS_eBMD were significantly associated with MOF and HF risk, independent of the base FRAX risk factors. Compare to the base FRAX model, the models with GRS_fracture, GRS_BMD, and GRS_eBMD improved the reclassification of MOF by 0.5% (95% CI, 0.2% to 0.9%, p = p < .01), 0.3% (95% CI, 0.1% to 0.6%, p = 0.01), and 2.1% (95% CI, 0.3% to 2.8%, p < .01), respectively. Similar results were also observed when using HF as an outcome. Our study suggested that the addition of genetic profiles provide limited improvements in the reclassification of FRAX for MOF and HF.

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Fig. 1

Data Availability

The data used in the current study is publically available through the database of Genotype and Phenotype (dbGap) (


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The data/analyses presented in the current publication are based on the use of study data downloaded from the dbGaP website, under phs000200 ( The research and analysis described in the current publication were supported by the Genome Acquisition to Analytics Research Core of the Personalized Medicine Center of Biomedical Research Excellence in the Nevada Institute of Personalized Medicine, and the National Supercomputing Institute at the University of Nevada Las Vegas provided facilities for bioinformatical analysis in this study. The research and analysis described in the current publication were supported by a grant from the National Institute of General Medical Sciences (P20GM121325), a grant from the National Institute on Minority Health and Health Disparities of the National Institutes of Health (R15MD010475). The funding sponsors were not involved in the analysis design, genotype imputation, data analysis, interpretation of the analysis results, or the preparation, review, or approval of this manuscript.


The research was funded by a grant from the National Institute of General Medical Sciences (P20GM121325), a grant from the National Institute on Minority Health and Health Disparities of the National Institutes of Health (R15MD010475). The funding sponsors were not involved in the analysis design, genotype imputation, data analysis, interpretation of the analysis results, or the preparation, review, or approval of this manuscript.

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Correspondence to Qing Wu.

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Qing Wu and Xiangxue Xiao declare that they have no conflict of interest.

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This study analyzed de-identified, secondary data only, and was exempted by the Institutional Review Board at the University of Nevada, Las Vegas.

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Xiao, X., Wu, Q. The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women. Calcif Tissue Int (2021).

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  • Genetic risk score (GRS)
  • Bone mineral density (BMD)
  • Single nucleotide polymorphism (SNP)
  • Fracture risk assessment tool (FRAX)