Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study
Autoři:
Vincenzo Forgetta aff001; Julyan Keller-Baruch aff002; Marie Forest aff001; Audrey Durand aff003; Sahir Bhatnagar aff001; John P. Kemp aff004; Maria Nethander aff006; Daniel Evans aff008; John A. Morris aff001; Douglas P. Kiel aff009; Fernando Rivadeneira aff010; Helena Johansson aff011; Nicholas C. Harvey aff013; Dan Mellström aff007; Magnus Karlsson aff015; Cyrus Cooper aff013; David M. Evans aff004; Robert Clarke aff017; John A. Kanis aff011; Eric Orwoll aff018; Eugene V. McCloskey aff020; Claes Ohlsson aff007; Joelle Pineau aff003; William D. Leslie aff021; Celia M. T. Greenwood aff001; J. Brent Richards aff001
Působiště autorů:
Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
aff001; Department of Human Genetics, McGill University, Montreal, Quebec, Canada
aff002; School of Computer Science, McGill University, Montréal, Québec, Canada
aff003; University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
aff004; Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
aff005; Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
aff006; Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
aff007; California Pacific Medical Center Research Institute, San Francisco, California, United States of America
aff008; Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Broad Institute of MIT & Harvard University, Boston, Massachusetts, United States of America
aff009; Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Netherlands
aff010; Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
aff011; Australian Catholic University, Melbourne, Victoria, Australia
aff012; Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
aff013; National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
aff014; Department of Orthopaedics and Clinical Sciences, Lund University, Skane University Hospital, Malmö, Sweden
aff015; National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
aff016; Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, United Kingdom
aff017; Bone and Mineral Unit, Oregon Health & Science University, Portland, Oregon, United States of America
aff018; Department of Medicine, Oregon Health & Science University, Portland, Oregon, United States of America
aff019; Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield and Sheffield Teaching Hospitals Foundation Trust, Sheffield, United Kingdom
aff020; Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
aff021; Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
aff022; Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
aff023; Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
aff024
Vyšlo v časopise:
Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study. PLoS Med 17(7): e32767. doi:10.1371/journal.pmed.1003152
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003152
Souhrn
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 polygenic risk score for heel quantitative ultrasound speed of sound (SOS)—a heritable risk factor for osteoporotic fracture—can identify low-risk individuals who can safely be excluded from a fracture risk screening program.
Methods and findings
A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed “gSOS”, and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)–based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk.
Conclusions
Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.
Klíčová slova:
Bone fracture – Genetic screens – Genome-wide association studies – Genotyping – Medical risk factors – Osteoporosis – Screening guidelines – Traumatic injury risk factors
Zdroje
1. Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Am J Med. 1993;94: 646–50. doi: 10.1016/0002-9343(93)90218-e 8506892
2. Papaioannou A, Morin S, Cheung AM, Atkinson S, Brown JP, Feldman S, et al. 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ. 2010;182:1864–73. doi: 10.1503/cmaj.100771 20940232
3. Compston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017;12:43. doi: 10.1007/s11657-017-0324-5 28425085
4. Curry SJ, Krist AH, Owens DK, Barry MJ, Caughey AB, Davidson KW, et al. Screening for osteoporosis to prevent fractures us preventive services task force recommendation statement. JAMA. 2018;319:2521–31. doi: 10.1001/jama.2018.7498 29946735
5. Cosman F, de Beur SJ, LeBoff MS, Lewiecki EM, Tanner B, Randall S, et al. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25:2359–81. doi: 10.1007/s00198-014-2794-2 25182228
6. Kanis JA, Harvey N, Cooper C, Johansson H, Odén A, McCloskey E, et al. A systematic review of intervention thresholds based on FRAX: a report prepared for the National Osteoporosis Guideline Group and the International Osteoporosis Foundation. Arch Osteoporos. 2016;11:25. doi: 10.1007/s11657-016-0278-z 27465509
7. Kanis JA. Assessment of osteoporosis at the primary health care level. WHO Scientific Group Technical Report. Sheffield (UK): World Health Organization Collaborating Centre for Metabolic Bone Diseases; 2007. https://www.sheffield.ac.uk/FRAX/pdfs/WHO_Technical_Report.pdf.
8. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and woman from the UK. Osteoporos Int. 2008;19:385–97. doi: 10.1007/s00198-007-0543-5 18292978
9. Shepstone L, Lenaghan E, Cooper C, Clarke S, Fong-Soe-Khioe R, Fordham R, et al. Screening in the community to reduce fractures in older women (SCOOP): a randomised controlled trial. Lancet. 2018;391:741–7. doi: 10.1016/S0140-6736(17)32640-5 29254858
10. Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature. 2015;526112–7. doi: 10.1038/nature14878 26367794
11. Kemp JP, Morris JA, Medina-Gomez C, Forgetta V, Warrington NM, Youlten SE, et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet. 2017;49:1468–75. doi: 10.1038/ng.3949 28869591
12. Richards JB, Zheng HF, Spector TD. Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat Rev Genet. 2012;13:672. doi: 10.1038/nrg3315
13. Howard GM, Nguyen TV, Harris M, Kelly PJ, Eisman JA. Genetic and environmental contributions to the association between quantitative ultrasound and bone mineral density measurements: a twin study. J Bone Miner Res. 1998;13:1318–27. doi: 10.1359/jbmr.1998.13.8.1318 9718201
14. Kim SK. Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture. PLoS ONE. 2018;13:e0200785. doi: 10.1371/journal.pone.0200785 30048462
15. Evans DM, Visscher PM, Wray NR. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum Mol Genet. 2009;18:3525–31. doi: 10.1093/hmg/ddp295 19553258
16. Khera AV, Chaffin M, Aragam K, Haas M, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24. doi: 10.1038/s41588-018-0183-z 30104762
17. Inouye M, Abraham G, Nelson CP, Wood AM, Sweeting MJ, Dudbridge F, et al. Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention. bioRxiv 250712. 2018 Jan 19. doi: 10.1101/250712
18. Thériault S, Lali R, Chong M, Velianou JL, Natarajan MK, Paré G. Polygenic contribution in individuals with early-onset coronary artery disease. Circ Genom Precis Med. 2018;11:e001849. doi: 10.1161/CIRCGEN.117.001849 29874178
19. Seibert TM, Fan CC, Wang Y, Zuber V, Karunamuni R, Parsons JK, et al. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts. BMJ. 2018;360:j5757. doi: 10.1136/bmj.j5757 29321194
20. Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177:587–96.e9. doi: 10.1016/j.cell.2019.03.028 31002795
21. Office for Life Sciences. UK life sciences sector deal 2, 2018. London: HM Government; 2018.
22. Gonnelli S, Cepollaro C, Gennari L, Montagnani A, Caffarelli C, Merlotti D, et al. Quantitative ultrasound and dual-energy X-ray absorptiometry in the prediction of fragility fracture in men. Osteoporos Int. 2005;16:963–8. doi: 10.1007/s00198-004-1771-6 15599495
23. Tibshirani R. regression selection and shrinkage via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58:267–88.
24. Janssens ACJW, Ioannidis JPA, van Duijn CM, Little J, Khoury MJ. Strengthening the reporting of genetic risk prediction studies: the GRIPS statement. PLoS Med. 2011;8:e1000420. doi: 10.1371/journal.pmed.1000420 21423587
25. Morris JA, Kemp JP, Youlten SE, Laurent L, Logan JG, Chai RC, et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet. 2019;51:258–66. doi: 10.1038/s41588-018-0302-x 30598549
26. Kanis JA, Oden A, Johnell O, Johansson H, De Laet C, Brown J, et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int. 2007;18:1033–46. doi: 10.1007/s00198-007-0343-y 17323110
27. Riley RD, Ensor J, Snell KIE, Debray TPA, Altman DG, Moons KGM, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016;353:i3140. doi: 10.1136/bmj.i3140 27334381
28. Harvey NC, Matthews P, Collins R, Cooper C. Osteoporosis epidemiology in UK Biobank: a unique opportunity for international researchers. Osteoporosis Int. 2013;24:2903–5. doi: 10.1007/s00198-013-2508-1 24057481
29. Loh P-R, Tucker G, Bulik-Sullivan BK, Vilhjálmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47:284–90. doi: 10.1038/ng.3190 25642633
30. Kanis JA. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: synopsis of a WHO report. WHO Study Group. Osteoporos Int. 1994;4:368–81. doi: 10.1007/BF01622200 7696835
31. Johansson H, Kanis JA, Oden A, Compston J, McCloskey E. A comparison of case-finding strategies in the UK for the management of hip fractures. Osteoporos Int. 2012;23:907–15. doi: 10.1007/s00198-011-1864-y 22234810
32. Nayak S, Roberts MS, Greenspan SL. Cost-effectiveness of different screening strategies for osteoporosis in postmenopausal women. Ann Intern Med. 2011;155:751. doi: 10.7326/0003-4819-155-11-201112060-00007 22147714
33. Turner DA, Khioe RFS, Shepstone L, Lenaghan E, Cooper C, Gittoes N, et al. The cost-effectiveness of screening in the community to reduce osteoporotic fractures in older women in the UK: economic evaluation of the SCOOP study. J Bone Miner Res. 2018;33:845–51. doi: 10.1002/jbmr.3381 29470854
34. Söreskog E, Borgström F, Shepstone L, Clarke S, Cooper C, Harvey I, et al. Long-term cost-effectiveness of screening for fracture risk in a UK primary care setting: the SCOOP study. Osteoporos Int. 2020 Apr 1. doi: 10.1007/s00198-020-05372-6 32239237
35. Richards JB, Leslie WD, Joseph L, Siminoski K, Hanley DA, Adachi JD, et al. Changes to osteoporosis prevalence according to method of risk assessment. J Bone Miner Res. 2006;22:228–34. doi: 10.1359/JBMR.061109 17129177
36. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13:395–405. doi: 10.1038/nrg3208 22549152
37. Grzymski JJ, Coppes MJ, Metcalf J, Galanopoulos C, Rowan C, Henderson M, et al. The Healthy Nevada Project: rapid recruitment for population health study. bioRxiv 250274. 2018 Jan 19. doi: 10.1101/250274
38. Hunter DJ, Drazen JM. Has the genome granted our wish yet? N Engl J Med. 2019;380:2391–3. doi: 10.1056/NEJMp1904511 31091368
39. Estrada K, Styrkarsdottir U, Evangelou E, Hsu Y-H, Duncan EL, Ntzani EE, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet. 2012;44:491–501. doi: 10.1038/ng.2249 22504420
40. Eriksson J, Evans DS, Nielson CM, Shen J, Srikanth P, Hochberg M, et al. Limited clinical utility of a genetic risk score for the prediction of fracture risk in elderly subjects. J Bone Miner Res. 2015;30:184–94. doi: 10.1002/jbmr.2314 25043339
41. Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: current status and what it means for epidemiology. Health Policy Technol. 2012;1:123–6. doi: 10.1016/j.hlpt.2012.07.003
42. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91. doi: 10.1038/s41588-019-0379-x 30926966
Článek vyšel v časopise
PLOS Medicine
2020 Číslo 7
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- „Jednohubky“ z klinického výzkumu – 2024/44
- Je libo čepici místo mozkového implantátu?
Nejčtenější v tomto čísle
- Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank
- Participation in adherence clubs and on-time drug pickup among HIV-infected adults in Zambia: A matched-pair cluster randomized trial
- Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
- Neonatal outcome in 29 pregnant women with COVID-19: A retrospective study in Wuhan, China
