Improved prediction of fracture risk leveraging a genome-wide polygenic risk score

Abstract

Background Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. Methods We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. Results A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13–1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727–0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791–0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. Conclusions We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

A Polygenic Risk Score to Predict Future Adult Short Stature Among Children

“…In the UK Biobank test set consisting of 81 902 individuals ( Table 1 ), in combination with age, sex, recruitment center, genotyping array, and the first 20 genetic principal components (to account for population stratification ( 14 , 15 , 18 , 33 , 34 )), a polygenic risk score was able to capture 71.1% [95% confidence interval (CI): 70.8%-71.4%] of the total variance in measured adult height (supplementary notes and Supplementary Figure 1 in ( 28 )). In the ALSPAC cohort, among 941 children for whom mid-parental height prediction was available ( Table 1 ), the polygenic risk score, together with sex, explained 71.0% (95% CI: 67.9%-74.1%) of the total variance in adult height ( Fig.…”

Genetic determinants of polygenic prediction accuracy within a population

“…These polygenic risk scores aggregate multiple genetic variants associated with the target traits across the genome and may be able to capture a significant proportion of trait heritability [2,4]. It has been recognized that polygenic risk scores can contribute importantly both clinically and in research, by enabling risk stratification in large populations [2,[5][6][7], informing on risk factors [8,9], assisting diagnosis for complex diseases [10], and suggesting potential therapeutic targets [11].…”

Capturing additional genetic risk from family history for improved polygenic risk prediction

“…In recent years, large-scale genome-wide association studies (GWASs) have characterized the genetic architecture of many complex traits and diseases 3 . Developing polygenic risk scores aggregating the effects of well-profiled genetic determinants has become possible 4,5 , and polygenic risk scores have demonstrated the potential to improve risk stratification in large populations [6][7][8][9] , assist diagnosis and clinical differentiation 10,11 , and refine risk management and treatment strategies 12,13 .…”