Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study

Forgetta, Vincenzo; Keller-Baruch, Julyan; Forest, Marie; Durand, Audrey; Bhatnagar, Sahir; Kemp, John P.; Nethander, Maria; Evans, Daniel S.; Morris, John A.; Kiel, Douglas P.; Rivadeneira, Fernando; Johansson, Helena; Harvey, Nicholas C.; Mellström, Dan; Karlsson, Magnus K.; Cooper, Cyrus; Evans, David M.; Clarke, Robert; Kanis, John А.; Orwoll, Eric S.; McCloskey, Eugène; Ohlsson, Claes; Pineau, Joëlle; Leslie, William D.; Greenwood, Celia; Richards, J. Brent

doi:10.1371/journal.pmed.1003152

Cited by 55 publications

(56 citation statements)

References 42 publications

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“…Using data from the UK Biobank, we have previously developed a genome-wide polygenic risk score for SOS, called genomic SOS (gSOS), that captures 25.0% of the total variance in SOS [23]. This largely exceeded an existing polygenic risk score which explained 17.2% of the total variance and achieved an AUROC of 0.570 for identifying individuals with any type of fracture [24].…”

Section: Introductionmentioning

confidence: 99%

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

Forgetta

Keller-Baruch

et al. 2021

Genome Med

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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.

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Section: Introductionmentioning

confidence: 99%

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

Forgetta

Keller-Baruch

et al. 2021

Genome Med

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“…LASSO frameworks have been used to identify SNPs for polygenic risk scores of several phenotypes, including fracture risk 30 , type 2 diabetes 31 , and breast cancer 12 . In this work, we have extended the application of LASSO to select SNPs in a polygenic hazard model of prostate cancer from a list of candidates previously identified through logistic and time-to-event analysis.…”

Section: Discussionmentioning

confidence: 99%

Additional SNPs improve the performance of a polygenic hazard score for prostate cancer

Karunamuni¹,

Huynh‐Le²,

Fan³

et al. 2020

Preprint

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Background: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and Method: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. Results: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Conclusion: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

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“…If the empirical differences seen between racial groups were actually due to genetic differences, then race adjustment might be justified: different coefficients for different bodies. Whilst the aspiration that genetics might replace the need for race or ethnicity is worthy [8], its potential in osteoporosis is presently limited and restricted to only a single component of fracture risk (bone mineral density) [9].…”

Section: Understanding Ethnicitymentioning

confidence: 99%

FRAX and ethnicity

et al. 2020

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Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study

Abstract: Background Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a

Cited by 55 publications

References 42 publications

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

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

Additional SNPs improve the performance of a polygenic hazard score for prostate cancer

FRAX and ethnicity

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