{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:01:48Z","timestamp":1769763708331,"version":"3.49.0"},"posted":{"date-parts":[[2025,6,7]]},"group-title":"Genetic and Genomic Medicine","reference-count":36,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,6,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Importance<\/jats:title>\n                  <jats:p>Current fracture risk prediction tools, including the Fracture Risk Assessment Tool (FRAX), do not incorporate genetic risk factors limiting accuracy and contributing to misclassification and suboptimal care.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To develop and validate a novel genome-informed fracture risk assessment tool (Bayes-FRAX) integrating Bayesian genome-wide polygenic scores (GPS) into the established FRAX to enhance major osteoporotic fracture (MOF) prediction.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Design, Setting, and Participants<\/jats:title>\n                  <jats:p>This retrospective cohort study analyzed clinical and genetic data from 6,932 postmenopausal women enrolled in the Women\u2019s Health Initiative (1993-1998).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Exposures<\/jats:title>\n                  <jats:p>Integration of GPS derived using Polygenic Risk Score Continuous Shrinkage (PRS-CS) and Summary data-based Bayesian regression (SBayesR) into FRAX.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Main Outcomes and Measures<\/jats:title>\n                  <jats:p>Primary outcomes included the incidence of MOF. Predictive performance metrics assessed were area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), calibration slopes, Hosmer\u2013Lemeshow goodness-of-fit tests, net reclassification improvement (NRI), diagnostic sensitivity, decision curve analysis (DCA), and external validation metrics.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Of 6,932 women, 513 (7.4%) experienced MOF. Bayes-FRAX significantly improved prediction over standard FRAX based solely on clinical risk factors, increasing AUROC from 0.662 to 0.680 for both PRS-CS and SBayesR. AUPRC improved from 0.120 (FRAX) to 0.140 (PRS-CS) and 0.138 (SBayesR). Calibration slopes were ideal (GPS-PRS-CS: 1.00 [95% CI: 0.8569\u20131.1431]; GPS-SBayesR: 1.00 [95% CI: 0.8560\u20131.1437]). Bayes-FRAX reclassified 3.5% of women, 34% near the intervention threshold. NRI improved by 4.59% (SBayesR) and 4.34% (PRS-CS), largely from better classification of women who fractured (5.85% and 5.65%). Decision curve analyses demonstrated greater net clinical benefit at clinically relevant thresholds, notably at the 20% threshold. External validation in 852 independent White postmenopausal women confirmed robust generalizability, with GPS significantly associated with fracture risk (PRS-CS OR = 0.148, 95% CI: 0.052\u20130.411; SBayesR OR = 0.116, 95% CI: 0.040\u20130.324). Likelihood ratio tests also supported improved model fit after GPS inclusion (PRS-CS:\n                    <jats:italic>P<\/jats:italic>\n                    &lt; 0.001; SBayesR:\n                    <jats:italic>P<\/jats:italic>\n                    &lt;0.001). Sensitivity analysis without BMD demonstrated stable AUROC (0.74).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions and Relevance<\/jats:title>\n                  <jats:p>Integrating GPS into FRAX using Bayesian methods improved fracture risk prediction, reclassification, and decision-making. Bayes-FRAX provides a generalizable tool for personalized osteoporosis care, especially for women near treatment thresholds.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Key Points<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Question<\/jats:title>\n                    <jats:p>Does incorporating genome-wide polygenic scores using Bayesian methods improve fracture risk prediction beyond the traditional FRAX?<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>In this cohort study of 6,932 postmenopausal women, adding Bayesian polygenic scores significantly improved prediction accuracy, calibration, and clinical reclassification, particularly among women older than 65 years, with robust external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Meaning<\/jats:title>\n                    <jats:p>Bayes-FRAX provides a personalized, more accurate fracture risk assessment, potentially improving clinical decision-making for postmenopausal women near treatment thresholds.<\/jats:p>\n                  <\/jats:sec>\n                <\/jats:sec>","DOI":"10.1101\/2025.06.06.25329139","type":"posted-content","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T21:35:11Z","timestamp":1749332111000},"source":"Crossref","is-referenced-by-count":1,"title":["Bayesian Genome-wide Polygenic Score Integration with FRAX for Enhanced Fracture Risk Prediction in Postmenopausal Women"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6807-3693","authenticated-orcid":false,"given":"Anqi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025061000300543000_2025.06.06.25329139v1.1","doi-asserted-by":"crossref","unstructured":"Clynes MA , Harvey NC , Curtis EM , et al. 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